pwc-archive/papers-with-abstracts huggingface dataset:
Stochastic Real-Time Economic Dispatch for Integrated Electric and Gas Systems Considering Uncertainty Propagation and Pipeline Leakage
Authors: eiyao Zhao, Zhengshuo Li, Jiahui Zhang, Xiang Bai, Jia Su
Date: 2024-08-15 00:00:00
Tasks: Data Interaction, Stochastic Optimization

Gas-fired units (GFUs) with rapid regulation capabilities are considered an effective tool to mitigate fluctuations in the generation of renewable energy sources and have coupled electricity power systems (EPSs) and natural gas systems (NGSs) more tightly. However, this tight coupling leads to uncertainty propagation, a challenge for the real-time dispatch of such integrated electric and gas systems (IEGSs). Moreover, pipeline leakage failures in the NGS may threaten the electricity supply reliability of the EPS through GFUs. To address these problems, this paper first establishes an operational model considering gas pipeline dynamic characteristics under uncertain leakage failures for the NGS and then presents a stochastic IEGS real-time economic dispatch (RTED) model considering both uncertainty propagation and pipeline leakage uncertainty. To quickly solve this complicated large-scale stochastic optimization problem, a novel notion of the coupling boundary dynamic adjustment region considering pipeline leakage failure (LCBDAR) is proposed to characterize the dynamic characteristics of the NGS boundary connecting GFUs. Based on the LCBDAR, a noniterative decentralized solution is proposed to decompose the original stochastic RTED model into two subproblems that are solved separately by the EPS and NGS operators, thus preserving their data privacy. In particular, only one-time data interaction from the NGS to the EPS is required. Case studies on several IEGSs at different scales demonstrate the effectiveness of the proposed method.
$\mathcal{H}_2$-optimal Model Reduction of Linear Quadratic Output Systems in Finite Frequency Range
Authors: Umair Zulfiqar, Zhi-Hua Xiao, Qiu-Yan Song, Mohammad Monir Uddin, Victor Sreeram
Date: 2024-08-15 00:00:00
Tasks: Computational Efficiency

In frequency-limited model order reduction, the objective is to maintain the frequency response of the original system within a specified frequency range in the reduced-order model. In this paper, a mathematical expression for the frequency-limited $\mathcal{H}_2$ norm is derived, which quantifies the error within the desired frequency interval. Subsequently, the necessary conditions for a local optimum of the frequency-limited $\mathcal{H}_2$ norm of the error are derived. The inherent difficulty in satisfying these conditions within a Petrov-Galerkin projection framework is also discussed. Using the optimality conditions and the Petrov-Galerkin projection, a stationary point iteration algorithm is proposed, which approximately satisfies these optimality conditions upon convergence. The main computational effort in the proposed algorithm involves solving sparse-dense Sylvester equations. These equations are frequently encountered in $\mathcal{H}_2$ model order reduction algorithms and can be solved efficiently. Moreover, the algorithm bypasses the requirement of matrix logarithm computation, which is typically necessary for most frequency-limited reduction methods and can be computationally demanding for high-order systems. An illustrative example is provided to numerically validate the developed theory. The proposed algorithm's effectiveness in accurately approximating the original high-order model within the specified frequency range is demonstrated through the reduction of an advection-diffusion equation-based model, commonly used in model reduction literature for testing algorithms. Additionally, the algorithm's computational efficiency is highlighted by successfully reducing a flexible space structure model of order one million.
Enhanced Equivalent Circuit Model for High Current Discharge of Lithium-Ion Batteries with Application to Electric Vertical Takeoff and Landing Aircraft
Authors: Alireza Goshtasbi, Ruxiu Zhao, Ruiting Wang, Sangwoo Han, Wenting Ma, Jeremy Neubauer
Date: 2024-08-15 00:00:00

Conventional battery equivalent circuit models (ECMs) have limited capability to predict performance at high discharge rates, where lithium depleted regions may develop and cause a sudden exponential drop in the cell's terminal voltage. Having accurate predictions of performance under such conditions is necessary for electric vertical takeoff and landing (eVTOL) aircraft applications, where high discharge currents can be required during fault scenarios and the inability to provide these currents can be safety-critical. To address this challenge, we utilize data-driven modeling methods to derive a parsimonious addition to a conventional ECM that can capture the observed rapid voltage drop with only one additional state. We also provide a detailed method for identifying the resulting model parameters, including an extensive characterization data set along with a well-regularized objective function formulation. The model is validated against a novel data set of over 150 flights encompassing a wide array of conditions for an eVTOL aircraft using an application-specific and safety-relevant reserve duration metric for quantifying accuracy. The model is shown to predict the landing hover capability with an error mean and standard deviation of 2.9 and 6.2 seconds, respectively, defining the model's ability to capture the cell voltage behavior under high discharge currents.
Stable State Space SubSpace (S$^5$) Identification
Authors: Xinhui Rong, Victor Solo
Date: 2024-08-15 00:00:00

State space subspace algorithms for input-output systems have been widely applied but also have a reasonably well-developedasymptotic theory dealing with consistency. However, guaranteeing the stability of the estimated system matrix is a major issue. Existing stability-guaranteed algorithms are computationally expensive, require several tuning parameters, and scale badly to high state dimensions. Here, we develop a new algorithm that is closed-form and requires no tuning parameters. It is thus computationally cheap and scales easily to high state dimensions. We also prove its consistency under reasonable conditions.
On Accelerating Large-Scale Robust Portfolio Optimization
Authors: Chung-Han Hsieh, Jie-Ling Lu
Date: 2024-08-15 00:00:00
Tasks: Portfolio Optimization

Solving large-scale robust portfolio optimization problems is challenging due to the high computational demands associated with an increasing number of assets, the amount of data considered, and market uncertainty. To address this issue, we propose an extended supporting hyperplane approximation approach for efficiently solving a class of distributionally robust portfolio problems for a general class of additively separable utility functions and polyhedral ambiguity distribution set, applied to a large-scale set of assets. Our technique is validated using a large-scale portfolio of the S&P 500 index constituents, demonstrating robust out-of-sample trading performance. More importantly, our empirical studies show that this approach significantly reduces computational time compared to traditional concave Expected Log-Growth (ELG) optimization, with running times decreasing from several thousand seconds to just a few. This method provides a scalable and practical solution to large-scale robust portfolio optimization, addressing both theoretical and practical challenges.
Optimization and Simulation of Startup Control for Space Nuclear Power Systems with Closed Brayton Cycle based on NuHeXSys
Authors: Chengyuan Li, Leran Guo, Shanfang Huang, Jian Deng
Date: 2024-08-14 00:00:00

This paper presents the development and optimization of a Space Nuclear Power System (SNPS) utilizing a helium-xenon gas-cooled Closed Brayton Cycle (CBC). A comprehensive dynamic system analysis code NuHeXSys (Nuclear Helium-Xenon Brayton Cycle Power System) was created, integrating non-ideal gas properties, a multi-channel thermal-hydraulic reactor core, and detailed turbo-machinery components. The innovation lies in parametrization of startup control sequence and application of an evolutionary algorithm (NSGA-II) to improve control performance, significantly reducing startup time and energy consumption. Model verification shows parameter deviations within 10%, confirming its accuracy. The optimized control strategy reduced startup time by 1260 seconds and lowered external energy demand by 17%, demonstrating improved efficiency and operational stability for deep space missions. This work provides a foundation for future advancements in optimizing space nuclear power systems.
Certifiable Reachability Learning Using a New Lipschitz Continuous Value Function
Authors: Jingqi Li, Donggun Lee, Jaewon Lee, Kris Shengjun Dong, Somayeh Sojoudi, Claire Tomlin
Date: 2024-08-15 00:00:00

We propose a new reachability learning framework for high-dimensional nonlinear systems, focusing on reach-avoid problems. These problems require computing the reach-avoid set, which ensures that all its elements can safely reach a target set despite disturbances within pre-specified bounds. Our framework has two main parts: offline learning of a newly designed reachavoid value function, and post-learning certification. Compared to prior work, our new value function is Lipschitz continuous and its associated Bellman operator is a contraction mapping, both of which improve the learning performance. To ensure deterministic guarantees of our learned reach-avoid set, we introduce two efficient post-learning certification methods. Both methods can be used online for real-time local certification or offline for comprehensive certification. We validate our framework in a 12-dimensional crazyflie drone racing hardware experiment and a simulated 10-dimensional highway take-over example.
Line zonotopes: A tool for state estimation and fault diagnosis of unbounded and descriptor systems
Authors: Brenner S. Rego, Davide M. Raimondo, Guilherme V. Raffo
Date: 2023-11-27 00:00:00
Tasks: Fault Diagnosis, State Estimation

This paper proposes new methods for set-based state estimation and active fault diagnosis (AFD) of linear descriptor systems (LDS). Unlike intervals, ellipsoids, and zonotopes, constrained zonotopes (CZs) can directly incorporate linear static constraints on state variables - typical of descriptor systems - into their mathematical representation, leading to less conservative enclosures. However, for LDS that are unstable or not fully observable, a bounded representation cannot ensure a valid enclosure of the states over time. To address this limitation, we introduce line zonotopes, a new representation for unbounded sets that retains key properties of CZs, including polynomial time complexity reduction methods, while enabling the description of strips, hyperplanes, and the entire n-dimensional Euclidean space. This extension not only generalizes the use of CZs to unbounded settings but can also enhance set-based estimation and AFD in both stable and unstable scenarios. Additionally, we extend the AFD method for LDS from Rego et al. (2020) to operate over reachable tubes rather than solely on the reachable set at the final time of the considered horizon. This reduces conservatism in input separation and enables more accurate fault diagnosis based on the entire output sequence. The advantages of the proposed methods over existing CZ-based approaches are demonstrated through numerical examples.
Local Cold Load Pick-up Estimation Using Customer Energy Consumption Measurements
Authors: Sanja Bajic, François Bouffard, Hannah Michalska, Géza Joós
Date: 2024-08-14 00:00:00
Tasks: Time Series Forecasting

Thermostatically-controlled loads have a significant impact on electricity demand after service is restored following an outage, a phenomenon known as cold load pick-up (CLPU). Active management of CLPU is becoming an essential tool for distribution system operators who seek to defer network upgrades and speed up post-outage customer restoration. One key functionality needed for actively managing CLPU is its forecast at various scales. The widespread deployment of smart metering devices is also opening up new opportunities for data-driven load modeling and forecast. In this paper, we propose an approach for customer-side estimation of CLPU using time-stamped local load measurements. The proposed method uses Auto-Regressive Integrated Moving Average (ARIMA) modeling for short-term foregone energy consumption forecast during an outage. Forecasts are made on an hourly basis to estimate the energy to potentially recover after outages lasting up to several hours. Moreover, to account for changing customer behavior and weather, the model order is adjusted dynamically. Simulation results based on actual smart meter measurements are presented for 50 residential customers over the duration of one year. These results are validated using physical modeling of residential loads and are shown to match well the ARIMA-based forecasts. Additionally, accuracy and execution speed has been compared with other state-of-the-art approaches for time-series forecasting including Long Short Term Memory Network (LSTM) and Holt-Winters Exponential Smoothing (HWES). ARIMA-based forecast is found to offer superior performance both in terms of accuracy and computation speed.
Model-Based Control of Water Treatment with Pumped Water Storage
Authors: Ryan Mauery, Margaret Busse, Ilya Kovalenko
Date: 2024-08-14 00:00:00
Tasks: Management, Model Predictive Control, Scheduling

Water treatment facilities are critical infrastructure they must accommodate dynamic demand patterns without system disruption. These patterns can be scheduled, such as daily residential irrigation, or unexpected, such as demand spikes from withdrawals for fire management. The critical necessity of clean, safe, and reliable water requires water treatment control strategies that are insensitive to disturbances to guarantee that demand will be met. One essential problem in achieving this is the minimization of energy costs in the process of meeting water demand, especially as the need for decarbonization persists. This work develops a control-oriented hydraulic model of a water treatment facility with integrated pumped storage and introduces a model predictive control strategy for scheduling treatment plant system operations to minimize greenhouse gas emissions and safely meet water demand.
A complex spatial frequency approach to optimal control of finite-extent linear evolution systems
Authors: Zhexian Li, Athanassios S. Fokas, Ketan Savla
Date: 2024-08-14 00:00:00

We consider the linear quadratic regulator (LQR) for one-dimensional linear evolution partial differential equations (PDEs) on a finite interval in space. The control is applied as an additive forcing term to PDEs. Existing methods for closed-form optimal control only apply to homogeneous (zero) boundary conditions, often resulting in series representations. In this paper, we consider general smooth boundary conditions. We use the unified transform, namely the Fourier transform restricted to the bounded spatial domain, to decouple PDEs into a family of ordinary differential equations (ODEs) parameterized by complex spatial frequency variables. Then, optimal control in the frequency domain is derived using LQR theory for ODEs. The inverse Fourier transform leads to non-causal terms in optimal control corresponding to integrals, over the real line, of future values of unspecified boundary conditions. To eliminate this non-causality, we deform the integrals to well-constructed contours in the complex plane along which the contribution of unknowns vanishes. For the reaction-diffusion equation, we show that the integral representation can be reformulated as a series representation, which leads to a state-feedback convolution form for optimal control, with the boundary conditions appearing as an additive term. In numerical experiments, we illustrate the computational advantages of the integral representation in comparison to the series representation and structural properties of the convolution kernel.
Microgrid Building Blocks for Dynamic Decoupling and Black Start Applications
Authors: Samrat Acharya, Priya Mana, Hisham Mahmood, Francis Tuffner, Alok Kumar Bharati
Date: 2024-08-14 00:00:00

Microgrids offer increased self-reliance and resilience at the grid's edge. They promote a significant transition to decentralized and renewable energy production by optimizing the utilization of local renewable sources. However, to maintain stable operations under all conditions and harness microgrids' full economic and technological potential, it is essential to integrate with the bulk grid and neighboring microgrids seamlessly. In this paper, we explore the capabilities of Back-to-Back (BTB) converters as a pivotal technology for interfacing microgrids, hybrid AC/DC grids, and bulk grids, by leveraging a comprehensive phasor-domain model integrated into GridLAB-D. The phasor-domain model is computationally efficient for simulating BTB with bulk grids and networked microgrids. We showcase the versatility of BTB converters (an integrated Microgrid Building Block) by configuring a two-microgrid network from a modified IEEE 13-node distribution system. These microgrids are equipped with diesel generators, photovoltaic units, and Battery Energy Storage Systems (BESS). The simulation studies are focused on use cases demonstrating dynamic decoupling and controlled support that a microgrid can provide via a BTB converter.
Steady-State Cascade Operators and their Role in Linear Control, Estimation, and Model Reduction Problems
Authors: John W. Simpson-Porco, Daniele Astolfi, Giordano Scarciotti
Date: 2024-08-14 00:00:00

Certain linear matrix operators arise naturally in systems analysis and design problems involving cascade interconnections of linear time-invariant systems, including problems of stabilization, estimation, and model order reduction. We conduct here a comprehensive study of these operators and their relevant system-theoretic properties. The general theory is leveraged to delineate both known and new design methodologies for control and observation of cascades, and to characterize structural properties of reduced models. Several entirely new designs arise from this systematic categorization, including new recursive and low-gain design frameworks for observation of cascaded systems. The benefits of the results beyond the linear time-invariant setting are demonstrated through preliminary extensions for nonlinear systems, with an outlook towards the development of a similarly comprehensive nonlinear theory.
Remote Tube-based MPC for Tracking Over Lossy Networks
Authors: David Umsonst, Fernando S. Barbosa
Date: 2024-08-14 00:00:00
Tasks: Model Predictive Control

This paper addresses the problem of controlling constrained systems subject to disturbances in the case where controller and system are connected over a lossy network. To do so, we propose a novel framework that splits the concept of tube-based model predictive control into two parts. One runs locally on the system and is responsible for disturbance rejection, while the other runs remotely and provides optimal input trajectories that satisfy the system's state and input constraints. Key to our approach is the presence of a nominal model and an ancillary controller on the local system. Theoretical guarantees regarding the recursive feasibility and the tracking capabilities in the presence of disturbances and packet losses in both directions are provided. To test the efficacy of the proposed approach, we compare it to a state-of-the-art solution in the case of controlling a cartpole system. Extensive simulations are carried out with both linearized and nonlinear system dynamics, as well as different packet loss probabilities and disturbances. The code for this work is available at https://github.com/EricssonResearch/Robust-Tracking-MPC-over-Lossy-Networks
Object Tracking Incorporating Transfer Learning into Unscented and Cubature Kalman Filters
Authors: Omar Alotaibi, Brian L. Mark
Date: 2024-08-13 00:00:00
Tasks: Object Tracking, Transfer Learning

We present a novel filtering algorithm that employs Bayesian transfer learning to address the challenges posed by mismatched intensity of the noise in a pair of sensors, each of which tracks an object using a nonlinear dynamic system model. In this setting, the primary sensor experiences a higher noise intensity in tracking the object than the source sensor. To improve the estimation accuracy of the primary sensor, we propose a framework that integrates Bayesian transfer learning into an Unscented Kalman Filter (UKF) and a Cubature Kalman Filter (CKF). In this approach, the parameters of the predicted observations in the source sensor are transferred to the primary sensor and used as an additional prior in the filtering process. Our simulation results show that the transfer learning approach significantly outperforms the conventional isolated UKF and CKF. Comparisons to a form of measurement vector fusion are also presented.
Stochastic diffusion using mean-field limits to approximate master equations
Authors: Laurent Hébert-Dufresne, Matthew M. Kling, Samuel F. Rosenblatt, Stephanie N. Miller, P. Alexander Burnham, Nicholas W. Landry, Nicholas J. Gotelli, Brian J. McGill
Date: 2024-08-14 00:00:00

Stochastic diffusion is the noisy and uncertain process through which dynamics like epidemics, or agents like animal species, disperse over a larger area. Understanding these processes is becoming increasingly important as we attempt to better prepare for potential pandemics and as species ranges shift in response to climate change. Unfortunately, modeling of stochastic diffusion is mostly done through inaccurate deterministic tools that fail to capture the random nature of dispersal or else through expensive computational simulations. In particular, standard tools fail to fully capture the heterogeneity of the area over which this diffusion occurs. Rural areas with low population density require different epidemic models than urban areas; likewise, the edges of a species range require us to explicitly track low integer numbers of individuals rather than vague averages. In this work, we introduce a series of new tools called "mean-FLAME" models that track stochastic dispersion using approximate master equations that explicitly follow the probability distribution of an area of interest over all of its possible states, up to states that are active enough to be approximated using a mean-field model. In one limit, this approach is locally exact if we explicitly track enough states, and in the other limit collapses back to traditional deterministic models if we track no state explicitly. Applying this approach, we show how deterministic tools fail to capture the uncertainty around the speed of nonlinear dynamical processes. This is especially true for marginal areas that are close to unsuitable for diffusion, like the edge of a species range or epidemics in small populations. Capturing the uncertainty in such areas is key to producing accurate forecasts and guiding potential interventions.
The mechanical properties of nerves, the size of the action potential, and consequences for the brain
Authors: T. Heimburg
Date: 2024-08-14 00:00:00

The action potential is widely considered a purely electrical phenomenon. However, one also finds mechanical and thermal changes that can be observed experimentally. In particular, nerve membranes become thicker and axons contract. The spatial length of the action potential can be quite large, ranging from millimeters to many centimeters. This suggests to employ macroscopic thermodynamics methods to understand its properties. The pulse length is several orders of magnitude larger than the synaptic gap, larger than the distance of the nodes of Ranvier, and even larger than the size of many neurons such as pyramidal cells or brain stem motor neurons. Here, we review the mechanical changes in nerves, theoretical possibilities to explain them, and implications of a mechanical nerve pulse for the neuron and for the brain. In particular, the contraction of nerves gives rise to the possibility of fast mechanical synapses.