pwc-archive/papers-with-abstracts huggingface dataset:
A Stochastic-MILP dispatch optimization model for Concentrated Solar Thermal under uncertainty
Authors: Navid Mohammadzadeh, Huy Truong-Ba, Michael E. Cholette, Theodore A. Steinberg, Giampaolo Manzolini
Date: 2024-01-02 00:00:00

Concentrated Solar Thermal (CST) offers a promising solution for large-scale solar energy utilization as Thermal Energy Storage (TES) enables electricity generation independently of daily solar fluctuations, shifting to high-priced electricity intervals. The development of dispatch planning tools is mandatory to account for uncertainties associated with solar irradiation and electricity price forecasts as well as limited storage capacity. This study proposes the Stochastic Mixed Integer Linear Program (SMILP) to maximize expected profit within a specified scenario space. The SMILP scenario space is generated by different Empirical Cumulative Distribution Function percentiles of the potential solar energy to accumulate in storage and the expected profit is estimated using the Sample Average Approximation (SAA) method. SMILP exhibits robust performance, however, its computational time poses a challenge. Thus, three heuristic solutions are developed which run a set of deterministic optimizations on different historical weather profiles to generate candidate dispatching plans (DPs). The candidate DP with the best average performance on all profiles is then selected. The new methods were applied to a case study for a 115 MW CST plant in South Australia. When the historical database has a limited set of historical weather profiles, the SMILP achieves 6% to 9% higher profit than the closest benchmark when the DP is applied to novel weather conditions. With a large historical weather data, the performance of SMILP and Heuristic-2 becomes nearly identical because the SMILP can only utilize a limited number of trajectories for optimization without becoming computationally infeasible. In this case, Heuristic-2 emerges a practical alternative, since it provides similar average profit in a reasonable time (saving about 7 hours in computing time).
Socially Compliant Control of Autonomous Vehicles with Application to Eco-Driving
Authors: Shian Wang
Date: 2024-01-01 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles

Control design of autonomous vehicles (AVs) has mostly focused on achieving a prespecified goal for an individually controlled AV or for a swarm of cooperatively controlled AVs. However, the impact of autonomous driving on human-driven vehicles (HVs) has been largely ignored in AV controller synthesis, which could result in egoistic AV behavior detrimental to the safety of passengers and surrounding traffic. In this study we develop a general framework for socially compliant control design of AVs with a useful metric of social psychology, called social value orientation (SVO), allowing AVs to leverage their impact on the behavior of the following HVs. This is critical since AVs that behave in a socially compliant manner enable human drivers to comprehend their actions and respond appropriately. Within the proposed framework, we define the utilities of the controlled AV and its following vehicle, to be maximized in a weighted fashion determined by the AV's SVO. The utility maximization covers an array of design objectives given the goal of the AV and the benefits for the following HV stemming from the courtesy of socially compliant AV controls. An optimal control problem is then formulated to maximize the utility function defined, which is numerically solved using Pontryagin's minimum principle with optimality guarantees. The methodology developed is applied to synthesize socially compliant control for eco-driving of AVs. A set of numerical results are presented to show the mechanism and effectiveness of the proposed approach using real-world experimental data collected on Highway 55 in Minnesota.
Noise-Aware and Equitable Urban Air Traffic Management: An Optimization Approach
Authors: Zhenyu Gao, Yue Yu, Qinshuang Wei, Ufuk Topcu, John-Paul Clarke
Date: 2024-01-01 00:00:00
Tasks: Fairness, Management

Urban air mobility (UAM), a transformative concept for the transport of passengers and cargo, faces several integration challenges in complex urban environments. Community acceptance of aircraft noise is among the most noticeable of these challenges when launching or scaling up a UAM system. Properly managing community noise is fundamental to establishing a UAM system that is environmentally and socially sustainable. In this work, we develop a holistic and equitable approach to manage UAM air traffic and its community noise impact in urban environments. The proposed approach is a hybrid approach that considers a mix of different noise mitigation strategies, including limiting the number of operations, cruising at higher altitudes, and ambient noise masking. We tackle the problem through the lens of network system control and formulate a multi-objective optimization model for managing traffic flow in a multi-layer UAM network while concurrently pursuing demand fulfillment, noise control, and energy saving. Further, we use a social welfare function in the optimization model as the basis for the efficiency-fairness trade-off in both demand fulfillment and noise control. We apply the proposed approach to a comprehensive case study in the city of Austin and perform design trade-offs through both visual and quantitative analyses.
The Smooth Trajectory Estimator for LMB Filters
Authors: Hoa Van Nguyen, Tran Thien Dat Nguyen, Changbeom Shim, Marzhar Anuar
Date: 2024-01-01 00:00:00

This paper proposes a smooth-trajectory estimator for the labelled multi-Bernoulli (LMB) filter by exploiting the special structure of the generalised labelled multi-Bernoulli (GLMB) filter. We devise a simple and intuitive approach to store the best association map when approximating the GLMB random finite set (RFS) to the LMB RFS. In particular, we construct a smooth-trajectory estimator (i.e., an estimator over the entire trajectories of labelled estimates) for the LMB filter based on the history of the best association map and all of the measurements up to the current time. Experimental results under two challenging scenarios demonstrate significant tracking accuracy improvements with negligible additional computational time compared to the conventional LMB filter. The source code is publicly available at https://tinyurl.com/ste-lmb, aimed at promoting advancements in MOT algorithms.
Hybrid physics-informed metabolic cybergenetics: process rates augmented with machine-learning surrogates informed by flux balance analysis
Authors: Sebastián Espinel-Ríos, José L. Avalos
Date: 2024-01-01 00:00:00

Metabolic cybergenetics is a promising concept that interfaces gene expression and cellular metabolism with computers for real-time dynamic metabolic control. The focus is on control at the transcriptional level, serving as a means to modulate intracellular metabolic fluxes. Recent strategies in this field have employed constraint-based dynamic models for process optimization, control, and estimation. However, this results in bilevel dynamic optimization problems, which pose considerable numerical and conceptual challenges. In this study, we present an alternative hybrid physics-informed dynamic modeling framework for metabolic cybergenetics, aimed at simplifying optimization, control, and estimation tasks. By utilizing machine-learning surrogates, our approach effectively embeds the physics of metabolic networks into the process rates of structurally simpler macro-kinetic models coupled with gene expression. These surrogates, informed by flux balance analysis, link the domains of manipulatable intracellular enzymes to metabolic exchange fluxes. This ensures that critical knowledge captured by the system's metabolic network is preserved. The resulting models can be integrated into metabolic cybergenetic schemes involving single-level optimizations. Additionally, the hybrid modeling approach maintains the number of system states at a necessary minimum, easing the burden of process monitoring and estimation. Our hybrid physics-informed metabolic cybergenetic framework is demonstrated using a computational case study on the optogenetically-assisted production of itaconate by $\textit{Escherichia coli}$.
Auxiliary Network-Enabled Attack Detection and Resilient Control of Islanded AC Microgrid
Authors: Vaibhav Vaishnav, Anoop Jain, Dushyant Sharma
Date: 2023-12-30 00:00:00

This paper proposes a cyber-resilient distributed control strategy equipped with attack detection capabilities for islanded AC microgrids in the presence of bounded stealthy cyber attacks affecting both frequency and power information exchanged among neighboring distributed generators (DGs). The proposed control methodology relies on the construction of an auxiliary layer and the establishment of effective inter-layer cooperation between the actual DGs in the control layer and the virtual DGs in the auxiliary layer. This cooperation aims to achieve robust frequency restoration and proportional active power-sharing. It is shown that the in situ presence of a concealed auxiliary layer not only guarantees resilience against stealthy bounded attacks on both frequency and power-sharing but also facilitates a network-enabled attack identification mechanism. The paper provides rigorous proof of the stability of the closed-loop system and derives bounds for frequency and power deviations under attack conditions, offering insights into the impact of the attack signal, control and pinning gains, and network connectivity on the system's convergence properties. The performance of the proposed controllers is illustrated by simulating a networked islanded AC microgrid in a Simulink environment showcasing both attributes of attack resilience and attack detection.
Synced Parallel Control Paths for Multi-Task System Operation
Authors: Behrooz Mirafzal, Fariba Fateh
Date: 2023-12-30 00:00:00

Numerous systems require the capability to switch their operational modes seamlessly without any disruptions. The "Synced Parallel Control Paths" method is an innovative control system architecture designed for seamless mode switching. It features multiple parallel control paths: the primary path for essential operational references, and the auxiliary paths that continuously align with the primary, ensuring synchronized operation. This reduces operational disruptions common in traditional systems during mode transitions. This approach enhances system stability, reliability, and adaptability, making it a significant advancement in control technology for a variety of dynamic systems.
Physics-informed Graphical Neural Network for Power System State Estimation
Authors: Quang-Ha Ngo, Bang L. H. Nguyen, Tuyen V. Vu, Jianhua Zhang, Tuan Ngo
Date: 2023-12-29 00:00:00
Tasks: Graph Neural Network, State Estimation

State estimation is highly critical for accurately observing the dynamic behavior of the power grids and minimizing risks from cyber threats. However, existing state estimation methods encounter challenges in accurately capturing power system dynamics, primarily because of limitations in encoding the grid topology and sparse measurements. This paper proposes a physics-informed graphical learning state estimation method to address these limitations by leveraging both domain physical knowledge and a graph neural network (GNN). We employ a GNN architecture that can handle the graph-structured data of power systems more effectively than traditional data-driven methods. The physics-based knowledge is constructed from the branch current formulation, making the approach adaptable to both transmission and distribution systems. The validation results of three IEEE test systems show that the proposed method can achieve lower mean square error more than 20% than the conventional methods.
Characterizing the Role of Complex Power in Small-Signal Synchronization Stability of Multi-Converter Power Systems
Authors: Fuyilong Ma, Huanhai Xin, Zhiyi Li, Linbin Huang
Date: 2023-12-29 00:00:00

Small-signal synchronization instability (SSI) may be triggered when a grid-connected converter is operated in weak grids. This problem is highly related to the active and reactive power (referred to as complex power) generation or consumption of the converter. Such an instability phenomenon manifests as power oscillations within the bandwidth frequency of phase-locked loop (PLL). However, in a multi-converter power system (MCPS), it is challenging to characterize the role of each converter's complex power in SSI issues due to the high dimension of system dynamics. In this paper, we focus on the impact regularity of complex power on the small-signal synchronization stability of the MCPS. Firstly, we formulate the modeling of the MCPS and develop a systematical stability indicator to explicitly quantify the stability margin under various operating complex power. Then, based on the proposed stability indicator, the "resultant force" of complex power on the stability margin of the MCPS can be analytically characterized into a weighted summation form. Furthermore, we elaborate on the impact of each converter's active power and reactive power, respectively. Notably, we demonstrate the cancellation effect between the active power generation and consumption on the stability margin, which ultimately helps prevent SSI issues in the MCPS. The correctness of theoretical findings is well verified by simulations results.
Solving Decision-Dependent Games by Learning from Feedback
Authors: Killian Wood, Ahmed Zamzam, Emiliano Dall'Anese
Date: 2023-12-29 00:00:00
Tasks: Stochastic Optimization

This paper tackles the problem of solving stochastic optimization problems with a decision-dependent distribution in the setting of stochastic strongly-monotone games and when the distributional dependence is unknown. A two-stage approach is proposed, which initially involves estimating the distributional dependence on decision variables, and subsequently optimizing over the estimated distributional map. The paper presents guarantees for the approximation of the cost of each agent. Furthermore, a stochastic gradient-based algorithm is developed and analyzed for finding the Nash equilibrium in a distributed fashion. Numerical simulations are provided for a novel electric vehicle charging market formulation using real-world data.
Wasserstein Distributionally Robust Regret-Optimal Control in the Infinite-Horizon
Authors: Taylan Kargin, Joudi Hajar, Vikrant Malik, Babak Hassibi
Date: 2023-12-28 00:00:00

We investigate the Distributionally Robust Regret-Optimal (DR-RO) control of discrete-time linear dynamical systems with quadratic cost over an infinite horizon. Regret is the difference in cost obtained by a causal controller and a clairvoyant controller with access to future disturbances. We focus on the infinite-horizon framework, which results in stability guarantees. In this DR setting, the probability distribution of the disturbances resides within a Wasserstein-2 ambiguity set centered at a specified nominal distribution. Our objective is to identify a control policy that minimizes the worst-case expected regret over an infinite horizon, considering all potential disturbance distributions within the ambiguity set. In contrast to prior works, which assume time-independent disturbances, we relax this constraint to allow for time-correlated disturbances, thus actual distributional robustness. While we show that the resulting optimal controller is non-rational and lacks a finite-dimensional state-space realization, we demonstrate that it can still be uniquely characterized by a finite dimensional parameter. Exploiting this fact, we introduce an efficient numerical method to compute the controller in the frequency domain using fixed-point iterations. This method circumvents the computational bottleneck associated with the finite-horizon problem, where the semi-definite programming (SDP) solution dimension scales with the time horizon. Numerical experiments demonstrate the effectiveness and performance of our framework.
Nonlinear energy harvesting system with multiple stability
Authors: Yanwei Han, Zijian Zhang
Date: 2023-12-28 00:00:00
Tasks: Friction

The nonlinear energy harvesting systems of the forced vibration with an electron-mechanical coupling are widely used to capture ambient vibration energy and convert mechanical energy into electrical energy. However, the nonlinear response mechanism of the friction induced vibration (FIV) energy harvesting system with multiple stability and stick-slip motion is still unclear. In the current paper, a novel nonlinear energy harvesting model with multiple stability of single-, double- and triple-well potential is proposed based on V-shaped structure spring and the belt conveying system. The dynamic equations for the energy harvesting system with multiple stability and self-excited friction are established by using Euler-Lagrangian equations. Secondly, the nonlinear restoring force, friction force, and potential energy surfaces for static characteristics of the energy harvesting system are obtained to show the nonlinear varying stiffness, multiple equilibrium points, discontinuous behaviors and multiple well response. Then, the equilibrium surface of bifurcation sets of the autonomous system is given to show the third-order quasi zero stiffness (QZS3), fifth-order quasi zero stiffness (QZS5), double well (DW) and triple well (TW). Furthermore, the response amplitudes of charge, current, voltage and power of the forced electron-mechanical coupled vibration system for QZS3, QZS5, DW and TW are analyzed by using the numerically solution. Finally, a prototype of FIV energy harvesting system is manufactured and the experimental system is setup. The experimental work of static restoring force, damping force and electrical output are well agreeable with the numerical results, which testified the proposed FIV energy harvesting model.
Distributional Reinforcement Learning-based Energy Arbitrage Strategies in Imbalance Settlement Mechanism
Authors: Seyed Soroush Karimi Madahi, Bert Claessens, Chris Develder
Date: 2023-12-23 00:00:00
Tasks: Distributional Reinforcement Learning, Q-Learning, reinforcement-learning

Growth in the penetration of renewable energy sources makes supply more uncertain and leads to an increase in the system imbalance. This trend, together with the single imbalance pricing, opens an opportunity for balance responsible parties (BRPs) to perform energy arbitrage in the imbalance settlement mechanism. To this end, we propose a battery control framework based on distributional reinforcement learning (DRL). Our proposed control framework takes a risk-sensitive perspective, allowing BRPs to adjust their risk preferences: we aim to optimize a weighted sum of the arbitrage profit and a risk measure while constraining the daily number of cycles for the battery. We assess the performance of our proposed control framework using the Belgian imbalance prices of 2022 and compare two state-of-the-art RL methods, deep Q learning and soft actor-critic. Results reveal that the distributional soft actor-critic method can outperform other methods. Moreover, we note that our fully risk-averse agent appropriately learns to hedge against the risk related to the unknown imbalance price by (dis)charging the battery only when the agent is more certain about the price.
Modelling contagious viral dynamics: a kinetic approach based on mutual utility
Authors: Giulia Bertaglia, Lorenzo Pareschi, Giuseppe Toscani
Date: 2023-12-31 00:00:00

The temporal evolution of a contagious viral disease is modelled as the dynamic progression of different classes of population with individuals interacting pairwise. This interaction follows a binary mechanism typical of kinetic theory, wherein agents aim to improve their condition with respect to a mutual utility target. To this end, we introduce kinetic equations of Boltzmann-type to describe the time evolution of the probability distributions of the multi-agent system. The interactions between agents are defined using principles from price theory, specifically employing Cobb-Douglas utility functions for binary exchange and the Edgeworth box to depict the common exchange area where utility increases for both agents. Several numerical experiments presented in the paper highlight the significance of this mechanism in driving the phenomenon toward endemicity.
Scale invariance in early embryonic development
Authors: Miloš Nikolić, Victoria Antonetti, Feng Liu, Gentian Muhaxheri, Mariela D. Petkova, Martin Scheeler, Eric M. Smith, William Bialek, Thomas Gregor
Date: 2023-12-29 00:00:00
Tasks: Position

The body plan of the fruit fly is determined by the expression of just a handful of genes. We show that the spatial patterns of expression for several of these genes scale precisely with the size of the embryo. Concretely, discrete positional markers such as the peaks in striped patterns have absolute positions along the anterior-posterior axis that are proportional to embryo length, with better than 1% accuracy. Further, the information (in bits) that graded patterns of expression provide about position can be decomposed into information about fractional or scaled position and information about absolute position or embryo length; all of the available information is about scaled position, again with ~1% accuracy. These observations suggest that the underlying genetic network exhibits scale invariance in a deeper mathematical sense. Taking this mathematical statement seriously requires that the network dynamics have a zero mode, which connects to many other observations on this system.
Applying Machine Learning Models on Metrology Data for Predicting Device Electrical Performance
Authors: Bappaditya Dey, Anh Tuan Ngo, Sara Sacchi, Victor Blanco, Philippe Leray, Sandip Halder
Date: 2023-11-20 00:00:00

Moore Law states that transistor density will double every two years, which is sustained until today due to continuous multi-directional innovations, such as extreme ultraviolet lithography, novel patterning techniques etc., leading the semiconductor industry towards 3nm node and beyond. For any patterning scheme, the most important metric to evaluate the quality of printed patterns is EPE, with overlay being its largest contribution. Overlay errors can lead to fatal failures of IC devices such as short circuits or broken connections in terms of P2P electrical contacts. Therefore, it is essential to develop effective overlay analysis and control techniques to ensure good functionality of fabricated semiconductor devices. In this work we have used an imec N14 BEOL process flow using LELE patterning technique to print metal layers with minimum pitch of 48nm with 193i lithography. FF structures are decomposed into two mask layers (M1A and M1B) and then the LELE flow is carried out to make the final patterns. Since a single M1 layer is decomposed into two masks, control of overlay between the two masks is critical. The goal of this work is of two-fold as, (a) to quantify the impact of overlay on capacitance and (b) to see if we can predict the final capacitance measurements with selected machine learning models at an early stage. To do so, scatterometry spectra are collected on these electrical test structures at (a)post litho, (b)post TiN hardmask etch, and (c)post Cu plating and CMP. Critical Dimension and overlay measurements for line-space pattern are done with SEM post litho, post etch and post Cu CMP. Various machine learning models are applied to do the capacitance prediction with multiple metrology inputs at different steps of wafer processing. Finally, we demonstrate that by using appropriate machine learning models we are able to do better prediction of electrical results.