pwc-archive/papers-with-abstracts huggingface dataset:
A Resilient Power Distribution System using P2P Energy Sharing
Authors: K. Victor Sam Moses Babu, Divyanshi Dwivedi, Pratyush Chakraborty, Pradeep Kumar Yemula, Mayukha Pal
Date: 2023-03-16 00:00:00
Tasks: Management

The adoption of distributed energy resources (DERs) such as solar panels and wind turbines is transforming the traditional energy grid into a more decentralized system, where microgrids are emerging as a key concept. Peer-to-Peer (P2P) energy sharing in microgrids enhances the efficiency and flexibility of the overall system by allowing the exchange of surplus energy and better management of energy resources. This work analyzes the impact of P2P energy sharing for three cases - within a microgrid, with neighbouring microgrids, and all microgrids combined together in a distribution system. A standard IEEE 123 node test feeder integrated with renewable energy sources is partitioned into microgrids. For P2P energy sharing between microgrids, the results show significant benefits in cost, reduced energy dependence on the grid, and a significant improvement in the system's resilience. We also predicted the energy requirement for a microgrid to evaluate energy resilience for the control and operation of the microgrid. Overall, the analysis provides valuable insights into the performance and sustainability of microgrids with P2P energy sharing.
Physically Consistent Multiple-Step Data-Driven Predictions Using Physics-based Filters
Authors: Yingzhao Lian, Jicheng Shi, Colin N. Jones
Date: 2023-03-16 00:00:00
Tasks: Decision Making

(Extended Version) Data-driven control can facilitate the rapid development of controllers, offering an alternative to conventional approaches. In order to maintain consistency between any known underlying physical laws and a data-driven decision-making process, preprocessing of raw data is necessary to account for measurement noise and any inconsistencies it may introduce. In this paper, we present a physics-based filter to achieve this and demonstrate its effectiveness through practical applications, using real-world datasets collected in a building on the Ecole Polytechnique Federale de Lausanne (EPFL) campus. Two distinct use cases are explored: indoor temperature control and demand response bidding.
Physical and Economic Viability of Cryptocurrency Mining for Provision of Frequency Regulation: A Real-World Texas Case Study
Authors: Rayan El Helou, Ali Menati, Le Xie
Date: 2023-03-16 00:00:00
Tasks: Decision Making

Demand flexibility plays a pivotal role in modern power systems with high penetration of variable energy resources. In recent years, one of the fastest-growing flexible energy demands has been proof-of-work-based cryptocurrency mining facilities. Due to their competitive ramping capabilities and demonstrated flexibility, such fast-responding loads are capable of participating in frequency regulation services for the grid while simultaneously increasing their own operational revenue. In this paper, we investigate the physical and economic viability of employing cryptocurrency mining facilities to provide frequency regulation in large power systems. We quantify mining facilities' operational profit, and propose a decision-making framework to explore their optimal participation strategy and account for the most influential factors. We employ real-world ERCOT ancillary services data in our case study to investigate the conditions under which provision of frequency regulation in the Texas grid is profitable. We also perform transient level simulations using a synthetic Texas grid to demonstrate the competitiveness of mining facilities at frequency regulation provision.
Combined Machine Learning and Physics-Based Forecaster for Intra-day and 1-Week Ahead Solar Irradiance Forecasting Under Variable Weather Conditions
Authors: Hugo Riggs, Shahid Tufail, Mohd Tariq, Arif Sarwat
Date: 2023-03-16 00:00:00
Tasks: Ensemble Learning, Solar Irradiance Forecasting

Power systems engineers are actively developing larger power plants out of photovoltaics imposing some major challenges which include its intermittent power generation and its poor dispatchability. The issue is that PV is a variable generation source unless additional planning and system additions for mitigation of generation intermittencies. One underlying factor that can enhance the applications around mitigating distributed energy resource intermittency challenges is forecasting the generation output. This is challenging especially with renewable energy sources which are weather dependent as due to the random nature of weather variance. This work puts forth a forecasting model which uses the solar variables to produce a PV generation forecast and evaluates a set of machine learning models for this task. In this paper, a forecaster for irradiance prediction for intra-day is proposed. This forecaster is capable of forecasting 15 minutes and hourly irradiance up to one week ahead. The paper performed a correlation and sensitivity analysis of the strength of the relationship between local weather parameters and system generation. In this study performance of SVM, CART, ANN, and Ensemble learning were analyzed for the prediction of 15-minute intraday and day-ahead irradiance. The results show that SVM and Ensemble learning yielded the lowest MAE for 15-minute intraday and day-ahead irradiance, respectively.
Imitation and Transfer Learning for LQG Control
Authors: Taosha Guo, Abed AlRahman Al Makdah, Vishaal Krishnan, Fabio Pasqualetti
Date: 2023-03-16 00:00:00
Tasks: Transfer Learning

In this paper we study an imitation and transfer learning setting for Linear Quadratic Gaussian (LQG) control, where (i) the system dynamics, noise statistics and cost function are unknown and expert data is provided (that is, sequences of optimal inputs and outputs) to learn the LQG controller, and (ii) multiple control tasks are performed for the same system but with different LQG costs. We show that the LQG controller can be learned from a set of expert trajectories of length $n(l+2)-1$, with $n$ and $l$ the dimension of the system state and output, respectively. Further, the controller can be decomposed as the product of an estimation matrix, which depends only on the system dynamics, and a control matrix, which depends on the LQG cost. This data-based separation principle allows us to transfer the estimation matrix across different LQG tasks, and to reduce the length of the expert trajectories needed to learn the LQG controller to~$2n+m-1$ with $m$ the dimension of the inputs (for single-input systems with $l=2$, this yields approximately a $50\%$ reduction of the required expert data).
Data-Driven Deep Learning Based Feedback Linearization of Systems with Unknown Dynamics
Authors: Raktim Gautam Goswami, Prashanth Krishnamurthy, Farshad Khorrami
Date: 2023-03-15 00:00:00

A methodology is developed to learn a feedback linearization (i.e., nonlinear change of coordinates and input transformation) using a data-driven approach for a single input control-affine nonlinear system with unknown dynamics. We employ deep neural networks to learn the feedback law (input transformation) in conjunction with an extension of invertible neural networks to learn the nonlinear change of coordinates (state transformation). We also learn the matrices A and B of the transformed linear system and define loss terms to ensure controllability of the pair (A, B). The efficacy of our approach is demonstrated by simulations on several nonlinear systems. Furthermore, we show that state feedback controllers designed using the feedback linearized system yield expected closed-loop behavior when applied to the original nonlinear system, further demonstrating validity of the learned feedback linearization.
Reconfigurable Intelligent Surface-Assisted Cross-Layer Authentication for Secure and Efficient Vehicular Communications
Authors: Mahmoud A. Shawky, Syed Tariq Shah, Michael S. Mollel, Jalil R. Kazim, Muhammad Ali Imran, Qammer H. Abbasi, Shuja Ansari, Ahmad Taha
Date: 2023-03-15 00:00:00

Intelligent transportation systems increasingly depend on wireless communication, facilitating real-time vehicular communication. In this context, message authentication is crucial for establishing secure and reliable communication. However, security solutions must consider the dynamic nature of vehicular communication links, which fluctuate between line-of-sight (LoS) and non-line-of-sight (NLoS). In this paper, we propose a lightweight cross-layer authentication scheme that employs public-key infrastructure-based authentication for initial legitimacy detection while using keyed-based physical-layer re-authentication for message verification. However, the latter's detection probability (P_d) decreases with the reduction of the signal-to-noise ratio (SNR). Therefore, we examine using Reconfigurable Intelligent Surface (RIS) to enhance the SNR value directed toward the designated vehicle and consequently improve the P_d, especially for NLoS scenarios. We conducted theoretical analysis and practical implementation of the proposed scheme using a 1-bit RIS, consisting of 64 x 64 reflective units. Experimental results show a significant improvement in the P_d, increasing from 0.82 to 0.96 at SNR = - 6 dB for an orthogonal frequency division multiplexing system with 128 subcarriers. We also conducted informal and formal security analyses, using Burrows-Abadi-Needham (BAN)-logic, to prove the scheme's ability to resist passive and active attacks. Finally, the computation and communication comparisons demonstrate the superior performance of the proposed scheme compared to traditional crypto-based methods.
Rollout-Based Charging Strategy for Electric Trucks with Hours-of-Service Regulations (Extended Version)
Authors: Ting Bai, Yuchao Li, Karl H. Johansson, Jonas Mårtensson
Date: 2023-03-15 00:00:00

Freight drivers of electric trucks need to design charging strategies for where and how long to recharge the truck in order to complete delivery missions on time. Moreover, the charging strategies should be aligned with drivers' driving and rest time regulations, known as hours-of-service (HoS) regulations. This letter studies the optimal charging problems of electric trucks with delivery deadlines under HoS constraints. We assume that a collection of charging and rest stations is given along a pre-planned route with known detours and that the problem data are deterministic. The goal is to minimize the total cost associated with the charging and rest decisions during the entire trip. This problem is formulated as a mixed integer program with bilinear constraints, resulting in a high computational load when applying exact solution approaches. To obtain real-time solutions, we develop a rollout-based approximate scheme, which scales linearly with the number of stations while offering solid performance guarantees. We perform simulation studies over the Swedish road network based on realistic truck data. The results show that our rollout-based approach provides near-optimal solutions to the problem in various conditions while cutting the computational time drastically.
GRNN-based Real-time Fault Chain Prediction
Authors: Anmol Dwivedi, Ali Tajer
Date: 2023-03-15 00:00:00
Tasks: Prediction

This paper proposes a data-driven graphical framework for the real-time search of risky cascading fault chains (FCs). While identifying risky FCs is pivotal to alleviating cascading failures, the complex spatio-temporal dependencies among the components of the power system render challenges to modeling and analyzing FCs. Furthermore, the real-time search of risky FCs faces an inherent combinatorial complexity that grows exponentially with the size of the system. The proposed framework leverages the recent advances in graph recurrent neural networks to circumvent the computational complexities of the real-time search of FCs. The search process is formalized as a partially observable Markov decision process (POMDP), which is subsequently solved via a time-varying graph recurrent neural network (GRNN) that judiciously accounts for the inherent temporal and spatial structures of the data generated by the system. The key features of this structure include (i) leveraging the spatial structure of the data induced by the system topology, (ii) leveraging the temporal structure of data induced by system dynamics, and (iii) efficiently summarizing the system's history in the latent space of the GRNN. The proposed framework's efficiency is compared to the relevant literature on the IEEE 39-bus New England system and the IEEE 118-bus system.
Multi-Competitive Virus Spread over a Time-Varying Networked SIS Model with an Infrastructure Network
Authors: Sebin Gracy, YuAn Wang, Philip E. Pare, Cesar A Uribe
Date: 2023-03-15 00:00:00

We study the spread of multi-competitive viruses over a (possibly) time-varying network of individuals accounting for the presence of shared infrastructure networks that further enables transmission of the virus. We establish a sufficient condition for exponentially fast eradication of a virus for: 1) time-invariant graphs, 2) time-varying graphs with symmetric interactions between individuals and homogeneous virus spread across the network (same healing and infection rate for all individuals), and 3) directed and slowly varying graphs with heterogeneous virus spread (not necessarily same healing and infection rates for all individuals) across the network. Numerical examples illustrate our theoretical results and indicate that, for the time-varying case, violation of the aforementioned sufficient conditions could lead to the persistence of a virus.
Automatic Generation of Topology Diagrams for Strongly-Meshed Power Transmission Systems
Authors: Jingyu Wang, Jinfu Chen, Dongyuan Shi, Xianzhong Duan
Date: 2023-03-15 00:00:00

Topology diagrams are widely seen in power system applications, but their automatic generation is often easier said than done. When facing power transmission systems with strongly-meshed structures, existing approaches can hardly produce topology diagrams catering to the aesthetics of readers. This paper proposes an integrated framework for generating aesthetically-pleasing topology diagrams for power transmission systems. Input with a rough layout, the framework first conducts visibility region analysis to reduce line crossings and then solves a mixed-integer linear programming problem to optimize the arrangement of nodes. Given that the complexity of both modules is pretty high, simplification heuristics are also proposed to enhance the efficiency of the framework. Case studies on several power transmission systems containing up to 2,046 nodes demonstrate the capability of the proposed framework in generating topology diagrams conforming to aesthetic criteria in the power system community. Compared with the widespread force-directed algorithm, the proposed framework can preserve the relative positions of nodes in the original layout to a great extent, which significantly contributes to the identification of electrical elements on the diagrams. Meanwhile, the time consumption is acceptable for practical applications.
Simulation and design of shaped pulses beyond the piecewise-constant approximation
Authors: Uluk Rasulov, Anupama Acharya, Marina Carravetta, Guinevere Mathies, Ilya Kuprov
Date: 2023-03-14 00:00:00

Response functions of resonant circuits create ringing artefacts if their input changes rapidly. When physical limits of electromagnetic spectroscopies are explored, this creates two types of problems. Firstly, simulation: the system must be propagated accurately through every response transient, this may be computationally expensive. Secondly, optimal control: circuit response must be taken into account; it may be advantageous to design pulses that are resilient to such distortions. At the root of both problems is the popular piecewise-constant approximation for control sequences in the rotating frame; in magnetic resonance it has persisted since the earliest days and has become entrenched in the commercially available hardware. In this paper, we report an implementation and benchmarks of recent Lie-group methods that can efficiently simulate and optimise smooth control sequences.
Modeling and Analysis on Efficiency Degradation of Lithium-ion Batteries
Authors: Zihui Lin, Dagang Li
Date: 2023-03-13 00:00:00
Tasks: Management

Efficiency of Battery Energy Storage Systems (BESSs) is increasingly critical as renewable energy generation becomes more prevalent on the grid. Therefore, it is necessary to study the energy efficiency of lithium-ion batteries, which are typically used in BESSs. The purpose of this study is to propose the State of Efficiency (SOE) as a measure of how efficiently batteries transfer energy, and to analyze what factors affect the SOE of a battery throughout its lifetime. Using NASA's data set, we measure the SOE of Nickel-Cobalt-Aluminum (NCA) lithium-ion batteries by calculating the ratio of energy generated and consumed during discharge and charge phases. A linear trend was observed in the SOE trajectories, which is confirmed by the Mann-Kendall (MK) trend test. Following that, a linear SOE degradation model was presented. Further analysis shows that ambient temperature, discharge current, and cutoff voltage all affect SOE in different ways. Using the SOE and its behavior observed in this study, Battery Management Systems (BMS) can improve the energy efficiency of batteries by adjusting operating conditions or developing better management strategies.
Arithmetic Average Density Fusion -- Part III: Heterogeneous Unlabeled and Labeled RFS Filter Fusion
Authors: Tiancheng Li, Ruibo Yan, Kai Da, Hongqi Fan
Date: 2023-03-12 00:00:00

This paper proposes a heterogenous density fusion approach to scalable multisensor multitarget tracking where the inter-connected sensors run different types of random finite set (RFS) filters according to their respective capacity and need. These diverse RFS filters result in heterogenous multitarget densities that are to be fused with each other in a proper means for more robust and accurate detection and localization of the targets. Our approach is based on Gaussian mixture implementations where the local Gaussian components (L-GCs) are revised for PHD consensus, i.e., the corresponding unlabeled probability hypothesis densities (PHDs) of each filter best fit their average regardless of the specific type of the local densities. To this end, a computationally efficient, coordinate descent approach is proposed which only revises the weights of the L-GCs, keeping the other parameters unchanged. In particular, the PHD filter, the unlabeled and labeled multi-Bernoulli (MB/LMB) filters are considered. Simulations have demonstrated the effectiveness of the proposed approach for both homogeneous and heterogenous fusion of the PHD-MB-LMB filters in different configurations.
Learning Feasibility Constraints for Control Barrier Functions
Authors: Wei Xiao, Christos G. Cassandras, Calin A. Belta
Date: 2023-03-10 00:00:00
Tasks: Autonomous Driving

It has been shown that optimizing quadratic costs while stabilizing affine control systems to desired (sets of) states subject to state and control constraints can be reduced to a sequence of Quadratic Programs (QPs) by using Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs). In this paper, we employ machine learning techniques to ensure the feasibility of these QPs, which is a challenging problem, especially for high relative degree constraints where High Order CBFs (HOCBFs) are required. To this end, we propose a sampling-based learning approach to learn a new feasibility constraint for CBFs; this constraint is then enforced by another HOCBF added to the QPs. The accuracy of the learned feasibility constraint is recursively improved by a recurrent training algorithm. We demonstrate the advantages of the proposed learning approach to constrained optimal control problems with specific focus on a robot control problem and on autonomous driving in an unknown environment.
Nonequilibrium calcium dynamics optimizes the energetic efficiency of mitochondrial metabolism
Authors: Valérie Voorsluijs, Francesco Avanzini, Gianmaria Falasco, Massimiliano Esposito, Alexander Skupin
Date: 2023-03-15 00:00:00

Living organisms continuously harness energy to perform complex functions for their adaptation and survival while part of that energy is dissipated in the form of heat or chemical waste. Determining the energetic cost and the efficiency of specific cellular processes remains a largely open problem. Here, we analyze the efficiency of mitochondrial adenosine triphosphate (ATP) production through the tricarboxylic acid (TCA) cycle and oxidative phosphorylation that generates most of the cellular chemical energy in eukaryotes. The regulation of this pathway by calcium signaling represents a well-characterized example of a regulatory cross-talk that can affect the energetic output of a metabolic pathway, but its concrete energetic impact remains elusive. On the one hand, calcium enhances ATP production by activating key enzymes of the TCA cycle, but on the other hand calcium homeostasis depends on ATP availability. To evaluate how calcium signaling impacts the efficiency of mitochondrial metabolism, we propose a detailed kinetic model describing the calcium-mitochondria cross-talk and we analyze it using a nonequilibrium thermodynamic approach: after identifying the effective reactions driving mitochondrial metabolism out of equilibrium, we quantify the thermodynamic efficiency of the metabolic machinery for different physiological conditions. We find that calcium oscillations increase the efficiency with a maximum close to substrate-limited conditions, suggesting a compensatory effect of calcium signaling on the energetics of mitochondrial metabolism.
A Bayesian theory of market impact
Authors: Louis Saddier, Matteo Marsili
Date: 2023-03-15 00:00:00

The available liquidity at any time in financial markets falls largely short of the typical size of the orders that institutional investors would trade. In order to reduce the impact on prices due to the execution of large orders, traders in financial markets split large orders into a series of smaller ones, which are executed sequentially. The resulting sequence of trades is called a meta-order. Empirical studies have revealed a non-trivial set of statistical laws on how meta-orders affect prices, which include i) the square-root behaviour of the expected price variation with the total volume traded, ii) its crossover to a linear regime for small volumes, and iii) a reversion of average prices towards its initial value, after the sequence of trades is over. Here we recover this phenomenology within a minimal theoretical framework where the market sets prices by incorporating all information on the direction and speed of trade of the meta-order in a Bayesian manner. The simplicity of this derivation lends further support to the robustness and universality of market impact laws. In particular, it suggests that the square-root impact law originates from the over-estimation of order flows originating from meta-orders.
Stress-Induced Mutagenesis Can Further Boost Population Success in Static Ecology
Authors: Kien T. Pham, Duc M. Nguyen, Duy V. Tran, Vi D. Ao, Huy D. Tran, Tuan K. Do, Trung V. Phan
Date: 2023-03-16 00:00:00

We have developed a mathematical model that captures stress-induced mutagenesis, a fundamental aspect of pathogenic and neoplastic evolutionary dynamics, on the fitness landscape with multiple relevant genetic traits as a high-dimensional Euclidean space. In this framework, stress-induced mutagenesis manifests as a heterogeneous diffusion process. We show how increasing mutations, and thus reducing exploitation, in a static ecology with fixed carrying capacity and maximum growth rates, can paradoxically boost population size. Remarkably, this unexpected biophysical phenomenon applies universally to any number of traits.