pwc-archive/papers-with-abstracts huggingface dataset:
Differentially-Private Distributed Model Predictive Control of Linear Discrete-Time Systems with Global Constraints
Authors: Kaixiang Zhang, Yongqiang Wang, Ziyou Song, Zhaojian Li
Date: 2024-05-29 00:00:00
Tasks: Distributed Optimization, Model Predictive Control

Distributed model predictive control (DMPC) has attracted extensive attention as it can explicitly handle system constraints and achieve optimal control in a decentralized manner. However, the deployment of DMPC strategies generally requires the sharing of sensitive data among subsystems, which may violate the privacy of participating systems. In this paper, we propose a differentially-private DMPC algorithm for linear discrete-time systems subject to coupled global constraints. Specifically, we first show that a conventional distributed dual gradient algorithm can be used to address the considered DMPC problem but cannot provide strong privacy preservation. Then, to protect privacy against the eavesdropper, we incorporate a differential-privacy noise injection mechanism into the DMPC framework and prove that the resulting distributed optimization algorithm can ensure both provable convergence to a global optimal solution and rigorous $\epsilon$-differential privacy. In addition, an implementation strategy of the DMPC is designed such that the recursive feasibility and stability of the closed-loop system are guaranteed. Simulation results are provided to demonstrate the effectiveness of the developed approach.
Unlocking the Potential of Renewable Energy Through Curtailment Prediction
Authors: Bilge Acun, Brent Morgan, Henry Richardson, Nat Steinsultz, Carole-Jean Wu
Date: 2024-05-28 00:00:00
Tasks: Prediction

A significant fraction (5-15%) of renewable energy generated goes into waste in the grids around the world today due to oversupply issues and transmission constraints. Being able to predict when and where renewable curtailment occurs would improve renewable utilization. The core of this work is to enable the machine learning community to help decarbonize electricity grids by unlocking the potential of renewable energy through curtailment prediction.
Bifurcations in Latch-Mediated Spring Actuation (LaMSA) Systems
Authors: Vittal Srinivasan, Nak-seung Patrick Hyun
Date: 2024-05-28 00:00:00

In nature, different species of smaller animals produce ultra-fast movements to aid in their locomotion or protect themselves against predators. These ultra-fast impulsive motions are possible, as often times, there exist a small latch in the organism that could hold the potential energy of the system, and once released, generate an impulsive motion. These types of systems are classified as Latch Mediated Spring Actuated (LaMSA) systems, a multi-dimensional, multi-mode hybrid system that switches between a latched and an unlatched state. The LaMSA mechanism has been studied extensively in the field of biology and is observed in a wide range of animal species, such as the mantis shrimp, grasshoppers, and trap-jaw ants. In recent years, research has been done in mathematically modeling the LaMSA behavior with physical implementations of the mechanism. A significant focus is given to mimicking the physiological behavior of the species and following an end-to-end trajectory of impulsive motion. This paper introduces a foundational analysis of the theoretical dynamics of the contact latch-based LaMSA mechanism. The authors answer the question on what makes these small-scale systems impulsive, with a focus on the intrinsic properties of the system using bifurcations. Necessary and sufficient conditions are derived for the existence of the saddle fixed points. The authors propose a mathematical explanation for mediating the latch when a saddle node exists, and the impulsive behavior after the bifurcation happens.
A Pioneering Roadmap for ML-Driven Algorithmic Advancements in Electrical Networks
Authors: Jochen L. Cremer, Adrian Kelly, Ricardo J. Bessa, Milos Subasic, Panagiotis N. Papadopoulos, Samuel Young, Amar Sagar, Antoine Marot
Date: 2024-05-27 00:00:00

Advanced control, operation, and planning tools of electrical networks with ML are not straightforward. 110 experts were surveyed to show where and how ML algorithms could advance. This paper assesses this survey and research environment. Then, it develops an innovation roadmap that helps align our research community with a goal-oriented realisation of the opportunities that AI upholds. This paper finds that the R&D environment of system operators (and the surrounding research ecosystem) needs adaptation to enable faster developments with AI while maintaining high testing quality and safety. This roadmap serves system operators, academics, and labs advancing next-generation electrical network tools.
On the analysis of a higher-order Lotka-Volterra model: an application of S-tensors and the polynomial complementarity problem
Authors: Shaoxuan Cui, Qi Zhao, Guofeng Zhang, Hildeberto Jardón-Kojakhmetov, Ming Cao
Date: 2024-05-28 00:00:00

It is known that the effect of species' density on species' growth is non-additive in real ecological systems. This challenges the conventional Lotka-Volterra model, where the interactions are always pairwise and their effects are additive. To address this challenge, we introduce HOIs (Higher-Order Interactions) which are able to capture, for example, the indirect effect of one species on a second one correlating to a third species. Towards this end, we propose a general higher-order Lotka-Volterra model. We provide an existence result of a positive equilibrium for a non-homogeneous polynomial equation system with the help of S-tensors. Afterward, by utilizing the latter result, as well as the theory of monotone systems and results from the polynomial complementarity problem, we provide comprehensive results regarding the existence, uniqueness, and stability of the corresponding equilibrium. These results can be regarded as natural extensions of many analogous ones for the classical Lotka-Volterra model, especially in the case of full cooperation, competition among two factions, and pure competition. Finally, illustrative numerical examples are provided to highlight our contributions.
Volt-PF Control Mode for Distribution Feeder Voltage Management Under High Penetration of Distributed Energy Resources
Authors: Madhura Sondharangalla, Dan Moldovan, Raja Ayyanar
Date: 2024-05-28 00:00:00
Tasks: Management

Volt-VAr control is a popular method for mitigating overvoltage violations caused by high penetration of distributed energy resources (DERs) in distribution feeders. An inherent limitation of volt-VAr control is that the reactive power (Q) absorbed/injected by the DER is determined based only on the terminal voltage, without considering the active power (P) generated by the DER. This leads to an inequitable burden of Q support, in the sense that those DERs generating lower P, and hence contributing less to overvoltage issues, may be required to provide more than their share of $Q$ support. The resulting PF of these DERs is required to vary over a wide range, which many current DERs do not support. A new control scheme, namely volt-PF control, is proposed here where the Q support is inherently a function of both the voltage and $P$ from DERs, which alleviates the above concerns while limiting the PF variation within a narrow range of 0.9 to 1. The proposed scheme is validated through extensive static and dynamic simulations on a real, large (8000+ nodes) feeder with very high penetration (>200%) of DERs.The implementation of the new scheme in new and existing commercial hardware inverters is described.
Space-Filling Input Design for Nonlinear State-Space Identification
Authors: Máté Kiss, Roland Tóth, Maarten Schoukens
Date: 2024-05-28 00:00:00

The quality of a model resulting from (black-box) system identification is highly dependent on the quality of the data that is used during the identification procedure. Designing experiments for linear time-invariant systems is well understood and mainly focuses on the power spectrum of the input signal. Performing experiment design for nonlinear system identification on the other hand remains an open challenge as informativity of the data depends both on the frequency-domain content and on the time-domain evolution of the input signal. Furthermore, as nonlinear system identification is much more sensitive to modeling and extrapolation errors, having experiments that explore the considered operation range of interest is of high importance. Hence, this paper focuses on designing space-filling experiments i.e., experiments that cover the full operation range of interest, for nonlinear dynamical systems that can be represented in a state-space form using a broad set of input signals. The presented experiment design approach can straightforwardly be extended to a wider range of system classes (e.g., NARMAX). The effectiveness of the proposed approach is illustrated on the experiment design for a nonlinear mass-spring-damper system, using a multisine input signal.
Joint Radar Sensing, Location, and Communication Resources Optimization in 6G Network
Authors: Haijun Zhang, Bowen Chen, Xiangnan Liu, Chao Ren
Date: 2024-05-28 00:00:00
Tasks: State Estimation

The possibility of jointly optimizing location sensing and communication resources, facilitated by the existence of communication and sensing spectrum sharing, is what promotes the system performance to a higher level. However, the rapid mobility of user equipment (UE) can result in inaccurate location estimation, which can severely degrade system performance. Therefore, the precise UE location sensing and resource allocation issues are investigated in a spectrum sharing sixth generation network. An approach is proposed for joint subcarrier and power optimization based on UE location sensing, aiming to minimize system energy consumption. The joint allocation process is separated into two key phases of operation. In the radar location sensing phase, the multipath interference and Doppler effects are considered simultaneously, and the issues of UE's location and channel state estimation are transformed into a convex optimization problem, which is then solved through gradient descent. In the communication phase, a subcarrier allocation method based on subcarrier weights is proposed. To further minimize system energy consumption, a joint subcarrier and power allocation method is introduced, resolved via the Lagrange multiplier method for the non-convex resource allocation problem. Simulation analysis results indicate that the location sensing algorithm exhibits a prominent improvement in accuracy compared to benchmark algorithms. Simultaneously, the proposed resource allocation scheme also demonstrates a substantial enhancement in performance relative to baseline schemes.
Combining Off-White and Sparse Black Models in Multi-step Physics-based Systems Identification -- EXTENDED VERSION
Authors: Cesare Donati, Martina Mammarella, Fabrizio Dabbene, Carlo Novara, Constantino Lagoa
Date: 2024-05-28 00:00:00
Tasks: Computational Efficiency, parameter estimation

In this paper, we propose a unified framework for identifying interpretable nonlinear dynamical models that preserve physical properties. The proposed approach integrates physical principles with black-box basis functions to compensate for unmodeled dynamics, ensuring accuracy over long prediction horizons and computational efficiency. Additionally, we introduce penalty terms to enforce physical consistency and stability during training. We provide a comprehensive analysis of theoretical properties related to multi-step nonlinear system identification, establishing bounds on parameter estimation errors and conditions for gradient stability and sparsity recovery. The proposed framework demonstrates significant potential for improving model accuracy and reliability in various engineering applications, making a substantial step towards the effective use of combined off-white and sparse black models in system identification. The effectiveness of the proposed approach is demonstrated on a nonlinear system identification benchmark.
A Passive and Asynchronous Wake-up Receiver for Acoustic Underwater Communication
Authors: Lukas Schulthess, Philipp Mayer, Luca Benini, Michele Magno
Date: 2024-05-28 00:00:00

Establishing reliable data exchange in an underwater domain using energy and power-efficient communication methods is crucial and challenging. Radio frequencies are absorbed by the salty and mineral-rich water and optical signals are obstructed and scattered after short distances. In contrast, acoustic communication benefits from low absorption and enables communication over long distances. Underwater communication must match low power and energy requirements as underwater sensor systems must have a long battery lifetime and need to work reliably due to their deployment and maintenance cost. For long-term deployments, the sensors' overall power consumption is determined by the power consumption during idle state. It can be reduced by integrating asynchronous always-on wake-up circuits with nano-watt power consumption. However, this approach does reduce but not eliminate idle power consumption, leaving a margin for improvement. This paper presents a passive and asynchronous wake-up receiver for acoustic underwater communication enabling zero-power always-on listening. Zero-power listening is achieved by combining energy and information transmission using a low-power wake-up receiver that extracts energy out of the acoustic signal and eliminates radio frontend idle consumption. In-field evaluations demonstrate that the wake-up circuit requires only 63 uW to detect and compare an 8-bit UUID at a data rate of 200 bps up to a distance of 5 m and that the needed energy can directly be extracted from the acoustic signal.
A real/fast-time simulator for impact assessment of spoofing & jamming attacks on GNSS receivers
Authors: Ivan Iudice, Domenico Pascarella, Gianluca Corraro, Giovanni Cuciniello
Date: 2024-05-28 00:00:00

In aviation, the impact of threats is becoming increasingly significant, particularly for global navigation satellite system (GNSS). Two relevant GNSS threats are represented by jamming and spoofing. In order to evaluate the technological solutions to counter GNSS attacks, such attacks should be assessed by means of a proper GNSS threat simulator. This work shows the implementation and the testing results of a GNSS security impact simulator which injects the desired threat scenarios as a deviations on the GNSS actual measurements. The proposed simulator can be integrated in both real- and fast-time simulation environments. The provided results confirm the effectiveness of the simulator, and include in-flight demonstrations by means of a flight experimental vehicle.
Differential Voltage Analysis and Patterns in Parallel-Connected Pairs of Imbalanced Cells
Authors: Clement Wong, Andrew Weng, Sravan Pannala, Jeesoon Choi, Jason B. Siegel, Anna Stefanopoulou
Date: 2024-05-28 00:00:00
Tasks: Fault Detection

Diagnosing imbalances in capacity and resistance within parallel-connected cells in battery packs is critical for battery management and fault detection, but it is challenging given that individual currents flowing into each cell are often unmeasured. This work introduces a novel method useful for identifying imbalances in capacity and resistance within a pair of parallel-connected cells using only voltage and current measurements from the pair. Our method utilizes differential voltage analysis (DVA) when the pair is under constant current discharge and demonstrates that features of the pair's differential voltage curve (dV/dQ), namely its mid-to-high SOC dV/dQ peak's height and skewness, are sensitive to imbalances in capacity and resistance. We analyze and explain how and why these dV/dQ peak shape features change in response to these imbalances, highlighting that the underlying current imbalance dynamics resulting from these imbalances contribute to these changes. Ultimately, we demonstrate that dV/dQ peak shape features can identify the product of capacity imbalance and resistance imbalance, but cannot uniquely identify the imbalances. This work lays the groundwork for identifying imbalances in capacity and resistance in parallel-connected cell groups in battery packs, where commonly only a single current sensor is placed for each parallel cell group.
Regression Equilibrium in Electricity Markets
Authors: Vladimir Dvorkin
Date: 2024-05-28 00:00:00
Tasks: regression

In two-stage electricity markets, renewable power producers enter the day-ahead market with a forecast of future power generation and then reconcile any forecast deviation in the real-time market at a penalty. The choice of the forecast model is thus an important strategy decision for renewable power producers as it affects financial performance. In electricity markets with large shares of renewable generation, the choice of the forecast model impacts not only individual performance but also outcomes for other producers. In this paper, we argue for the existence of a competitive regression equilibrium in two-stage electricity markets in terms of the parameters of private forecast models informing the participation strategies of renewable power producers. In our model, renewables optimize the forecast against the day-ahead and real-time prices, thereby maximizing the average profits across the day-ahead and real-time markets. By doing so, they also implicitly enhance the temporal cost coordination of day-ahead and real-time markets. We base the equilibrium analysis on the theory of variational inequalities, providing results on the existence and uniqueness of regression equilibrium in energy-only markets. We also devise two methods to compute regression equilibrium: centralized optimization and a decentralized ADMM-based algorithm.
A Two-sided Model for EV Market Dynamics and Policy Implications
Authors: Haoxuan Ma, Brian Yueshuai He, Tomas Kaljevic, Jiaqi Ma
Date: 2024-05-27 00:00:00

The diffusion of Electric Vehicles (EVs) plays a pivotal role in mitigating greenhouse gas emissions, particularly in the U.S., where ambitious zero-emission and carbon neutrality objectives have been set. In pursuit of these goals, many states have implemented a range of incentive policies aimed at stimulating EV adoption and charging infrastructure development, especially public EV charging stations (EVCS). This study examines the indirect network effect observed between EV adoption and EVCS deployment within urban landscapes. We developed a two-sided log-log regression model with historical data on EV purchases and EVCS development to quantify this effect. To test the robustness, we then conducted a case study of the EV market in Los Angeles (LA) County, which suggests that a 1% increase in EVCS correlates with a 0.35% increase in EV sales. Additionally, we forecasted the future EV market dynamics in LA County, revealing a notable disparity between current policies and the targeted 80% EV market share for private cars by 2045. To bridge this gap, we proposed a combined policy recommendation that enhances EV incentives by 60% and EVCS rebates by 66%, facilitating the achievement of future EV market objectives.
Data-Driven Personalized Energy Consumption Range Estimation for Plug-in Hybrid Electric Vehicles in Urban Traffic
Authors: Mehmet Fatih Ozkan, James Farrell, Marcello Telloni, Luis Mendez, Radu Pirvan, Jeffrey P. Chrstos, Marcello Canova, Stephanie Stockar
Date: 2024-05-27 00:00:00
Tasks: Conformal Prediction, Prediction Intervals, quantile regression

In urban traffic environments, driver behaviors exhibit considerable diversity in vehicle operation, encompassing a range of acceleration and braking maneuvers as well as adherence to traffic regulations, such as speed limits. It is well-established that these intrinsic driving behaviors significantly influence vehicle energy consumption. Therefore, establishing a quantitative relationship between driver behavior and energy usage is essential for identifying energy-efficient driving practices and optimizing routes within urban traffic. This study introduces a data-driven approach to predict the equivalent fuel consumption of a plug-in hybrid electric vehicle (PHEV) based on an integrated model of driver behavior and vehicle energy consumption. Unlike traditional models that provide point predictions of fuel consumption, this approach uses Conformalized Quantile Regression (CQR) to offer prediction intervals that capture the variability and uncertainty in fuel consumption. These intervals reflect changes in fuel consumption, as well as variations in driver behavior, and vehicle and route conditions. To develop this model, driver-specific data were collected through a driver-in-the-loop simulator, which tested different human drivers responses. The CQR model was then trained and validated using the experimental data from the driver-in-the-loop simulator, augmented by the synthetic data generated from Monte Carlo simulations conducted using a calibrated microscopic driver behavior and vehicle energy model. The CQR model was evaluated by comparing its predictions of equivalent fuel consumption intervals with those of baseline prediction interval methods that rely solely on conformal prediction.
Design of a Rectangular Linear Microstrip Patch Antenna Array for 5G Communication
Authors: Muhammad Asfar Saeed, Augustine O. Nwajana
Date: 2024-05-27 00:00:00
Tasks: Dielectric Constant

This paper presents the design and characterization of a rectangular microstrip patch antenna array optimized for operation within the Ku-band frequency range. The antenna array is impedance-matched to 50 Ohms and utilizes a microstrip line feeding mechanism for excitation. The design maintains compact dimensions, with the overall antenna occupying an area of 29.5x7 mm. The antenna structure is modelled on an R03003 substrate material, featuring a dielectric constant of 3, a low-loss tangent of 0.0009, and a thickness of 1.574 mm. The substrate is backed by a conducting ground plane, and the array consists of six radiating patch elements positioned on top. Evaluation of the designed antenna array reveals a resonant frequency of 18GHz, with a -10 dB impedance bandwidth extending over 700MHz. The antenna demonstrates a high gain of 7.51dBi, making it well-suited for applications in 5G and future communication systems. Its compact form factor, cost-effectiveness, and broad impedance and radiation coverage further underscore its potential in these domains.
A Mobility Equity Metric for Multi-Modal Intelligent Transportation Systems
Authors: Heeseung Bang, Aditya Dave, Filippos N. Tzortzoglou, Andreas A. Malikopoulos
Date: 2024-05-27 00:00:00
Tasks: Fairness

In this paper, we introduce a metric to evaluate the equity in mobility and a routing framework to enhance the metric within multi-modal intelligent transportation systems. The mobility equity metric (MEM) simultaneously accounts for service accessibility and transportation costs to quantify the equity and fairness in a transportation network. Finally, we develop a system planner integrated with MEM that aims to distribute travel demand for the transportation network, resulting in a socially optimal mobility system. Our framework results in a transportation network that is efficient in terms of travel time, improves accessibility, and ensures equity in transportation.