pwc-archive/papers-with-abstracts huggingface dataset:
LR-FHSS Transceiver for Direct-to-Satellite IoT Communications: Design, Implementation, and Verification
Authors: Sooyeob Jung, Seongah Jeong, Jinkyu Kang, Gyeongrae Im, Sangjae Lee, Mi-Kyung Oh, Joon Gyu Ryu, Joonhyuk Kang
Date: 2024-03-21 00:00:00

This paper proposes a long range-frequency hopping spread spectrum (LR-FHSS) transceiver design for the Direct-to-Satellite Internet of Things (DtS-IoT) communication system. The DtS-IoT system has recently attracted attention as a promising nonterrestrial network (NTN) solution to provide high-traffic and low-latency data transfer services to IoT devices in global coverage. In particular, this study provides guidelines for the overall DtS-IoT system architecture and design details that conform to the Long Range Wide-Area Network (LoRaWAN). Furthermore, we also detail various DtS-IoT use cases. Considering the multiple low-Earth orbit (LEO) satellites, we developed the LR-FHSS transceiver to improve system efficiency, which is the first attempt in real satellite communication systems using LR-FHSS. Moreover, as an extension of our previous work with perfect synchronization, we applied a robust synchronization scheme against the Doppler effect and co-channel interference (CCI) caused by LEO satellite channel environments, including signal detection for the simultaneous reception of numerous frequency hopping signals and an enhanced soft-output-Viterbi-algorithm (SOVA) for the header and payload receptions. Lastly, we present proof-of-concept implementation and testbeds using an application-specific integrated circuit (ASIC) chipset and a field-programmable gate array (FPGA) that verify the performance of the proposed LR-FHSS transceiver design of DtS-IoT communication systems. The laboratory test results reveal that the proposed LR-FHSS-based framework with the robust synchronization technique can provide wide coverage, seamless connectivity, and high throughput communication links for the realization of future sixth-generation (6G) networks.
PE-GPT: A Physics-Informed Interactive Large Language Model for Power Converter Modulation Design
Authors: Fanfan Lin, Junhua Liu, Xinze Li, Shuai Zhao, Bohui Zhao, Hao Ma, Xin Zhang
Date: 2024-03-21 00:00:00
Tasks: In-Context Learning, Language Modeling, Language Modelling, Large Language Model

This paper proposes PE-GPT, a custom-tailored large language model uniquely adapted for power converter modulation design. By harnessing in-context learning and specialized tiered physics-informed neural networks, PE-GPT guides users through text-based dialogues, recommending actionable modulation parameters. The effectiveness of PE-GPT is validated through a practical design case involving dual active bridge converters, supported by hardware experimentation. This research underscores the transformative potential of large language models in power converter modulation design, offering enhanced accessibility, explainability, and efficiency, thereby setting a new paradigm in the field.
Performance-Guaranteed Solutions for Multi-Agent Optimal Coverage Problems using Submodularity, Curvature, and Greedy Algorithms
Authors: Shirantha Welikala, Christos G. Cassandras
Date: 2024-03-20 00:00:00
Tasks: Event Detection

We consider a class of multi-agent optimal coverage problems in which the goal is to determine the optimal placement of a group of agents in a given mission space so that they maximize a coverage objective that represents a blend of individual and collaborative event detection capabilities. This class of problems is extremely challenging due to the non-convex nature of the mission space and of the coverage objective. With this motivation, greedy algorithms are often used as means of getting feasible coverage solutions efficiently. Even though such greedy solutions are suboptimal, the submodularity (diminishing returns) property of the coverage objective can be exploited to provide performance bound guarantees. Moreover, we show that improved performance bound guarantees (beyond the standard (1-1/e) performance bound) can be established using various curvature measures of the coverage problem. In particular, we provide a brief review of all existing popular applicable curvature measures, including a recent curvature measure that we proposed, and discuss their effectiveness and computational complexity, in the context of optimal coverage problems. We also propose novel computationally efficient techniques to estimate some curvature measures. Finally, we provide several numerical results to support our findings and propose several potential future research directions.
When are Lossy Energy Storage Optimization Models Convex?
Authors: Feras Al Taha, Eilyan Bitar
Date: 2024-03-20 00:00:00

We examine a class of optimization problems involving the optimal operation of a single lossy energy storage system, where energy losses occur during charging and discharging. These inefficiencies typically lead to a nonconvex set of feasible charging and discharging power profiles. In this paper, we derive an equivalent reformulation of this class of optimization problems by eliminating the charging and discharging power variables and recasting the problem entirely in terms of the storage state-of-charge variables. We show that the feasible set of the proposed reformulation is always convex. We also provide sufficient conditions under which the objective function of the proposed reformulation is guaranteed to be convex. The conditions provided both unify and generalize many existing conditions for convexity in the literature.
Credit vs. Discount-Based Congestion Pricing: A Comparison Study
Authors: Chih-Yuan Chiu, Devansh Jalota, Marco Pavone
Date: 2024-03-20 00:00:00

Tolling, or congestion pricing, offers a promising traffic management policy for regulating congestion, but has also attracted criticism for placing outsized financial burdens on low-income users. Credit-based congestion pricing (CBCP) and discount-based congestion pricing (DBCP) policies, which respectively provide travel credits and toll discounts to low-income users on tolled roads, have emerged as promising mechanisms for reducing traffic congestion without worsening societal inequities. However, the optimal design of CBCP and DBCP policies, as well as their relative advantages and disadvantages, remain poorly understood. To address this, we study the effects of implementing CBCP and DBCP policies to route users on a network of multi-lane highways with tolled express lanes. We formulate a non-atomic routing game framework in which a subset of eligible users is granted toll relief in the form of a fixed budget or toll discount, while the remaining ineligible users must pay out-of-pocket. We prove the existence of Nash equilibrium traffic flow patterns corresponding to any given CBCP or DBCP policy. Under the additional assumption that eligible users have time-invariant VoTs, we provide a convex program to efficiently compute these equilibria. For networks consisting of a single edge, we identify conditions under which CBCP policies outperform DBCP policies (and vice versa), in the sense of improving eligible users' access to the express lane. Finally, we present empirical results from a CBCP pilot study of the San Mateo 101 Express Lane Project in California. Our empirical results corroborate our theoretical analysis of the impact of deploying credit-based and discount-based policies, and lend insights into the sensitivity of their impact with respect to the travel demand and users' VoTs.
Clustering Heuristics for Robust Energy Capacitated Vehicle Routing Problem (ECVRP)
Authors: Mark Pustilnik, Francesco Borrelli
Date: 2024-03-20 00:00:00
Tasks: Clustering

The paper presents an approach to solving the Robust Energy Capacitated Vehicle Routing Problem (RECVRP), focusing on electric vehicles and their limited battery capacity. A finite number of customers, each with their own demand, have to be serviced by an electric vehicle fleet while ensuring that none of the vehicles run out of energy. The time and energy it takes to travel between any two points is modeled as a random variable with known distribution. We propose a Mixed Integer Program (MIP) for computing an exact solution and introduce clustering heuristics to enhance the solution speed. This enables efficient re-planning of routes in dynamic scenarios. The methodology transforms the RECVRP into smaller problems, yielding good quality solutions quickly compared to existing methods. We demonstrate the effectiveness of this approach using a well-known benchmark problem set as well as a set of randomly generated problems.
On Optimal Management of Energy Storage Systems in Renewable Energy Communities
Authors: Giovanni Gino Zanvettor, Marco Casini, Antonio Vicino
Date: 2024-03-20 00:00:00
Tasks: Management

Renewable energy communities are legal entities involving the association of citizens, organizations and local businesses aimed at contributing to the green energy transition and providing social, environmental and economic benefits to their members. This goal is pursued through the cooperative efforts of the community actors and by increasing the local energy self-consumption. In this paper, the optimal energy community operation in the presence of energy storage units is addressed. By exploiting the flexibility provided by the storage facilities, the main task is to minimize the community energy bill by taking advantage of incentives related to local self-consumption. Optimality conditions are derived, and an explicit optimal solution is devised. Numerical simulations are provided to assess the performance of the proposed solution.
Adaptive Reconstruction of Nonlinear Systems States via DREM with Perturbation Annihilation
Authors: Anton Glushchenko, Konstantin Lastochkin
Date: 2024-03-20 00:00:00

A new adaptive observer is proposed for a certain class of nonlinear systems with bounded unknown input and parametric uncertainty. Unlike most existing solutions, the proposed approach ensures asymptotic convergence of the unknown parameters, state and perturbation estimates to an arbitrarily small neighborhood of the equilibrium point. The solution is based on the novel augmentation of a high-gain observer with the dynamic regressor extension and mixing (DREM) procedure enhanced with a perturbation annihilation algorithm. The aforementioned properties of the proposed solution are verified via numerical experiments.
Macroscopic pricing schemes for the utilization of pool ride-hailing vehicles in bus lanes
Authors: Lynn Fayed, Gustav Nilsson, Nikolas Geroliminis
Date: 2024-03-20 00:00:00

With the increasing popularity of ride-hailing services, new modes of transportation are having a significant impact on the overall performance of transportation networks. As a result, there is a need to ensure that both the various transportation alternatives and the spatial network resources are used efficiently. In this work, we analyze a network configuration where part of the urban transportation network is devoted to dedicated bus lanes. Apart from buses, we let pool ride-hailing trips use the dedicated bus lanes which, contingent upon the demand for the remaining modes, may result in faster trips for users opting for the pooling alternative. Under an aggregated modelling framework, we characterize the spatial configuration and the multi-modal demand split for which this strategy achieves a system optimum. For these specific scenarios, we compute the equilibrium when ride-hailing users can choose between solo and pool services, and we provide a pricing scheme for mitigating the gap between total user delays of the system optimum and user equilibrium solutions, when needed.
Priority-based Energy Allocation in Buildings through Distributed Model Predictive Control
Authors: Hongyi Li, Jun Xu, Qianchuan Zhao
Date: 2024-03-20 00:00:00
Tasks: Model Predictive Control

Many countries are facing energy shortage today and most of the global energy is consumed by HVAC systems in buildings. For the scenarios where the energy system is not sufficiently supplied to HVAC systems, a priority-based allocation scheme based on distributed model predictive control is proposed in this paper, which distributes the energy rationally based on priority order. According to the scenarios, two distributed allocation strategies, i.e., one-to-one priority strategy and multi-to-one priority strategy, are developed in this paper and validated by simulation in a building containing three zones and a building containing 36 rooms, respectively. Both priority-based strategies fully exploit the potential of predictive control solutions. The experiment shows that our scheme has good scalability and achieve the performance of centralized strategy while making the calculation tractable.
3D Directed Formation Control with Global Shape Convergence using Bispherical Coordinates
Authors: Omid Mirzaeedodangeh, Farhad Mehdifar, Dimos V. Dimarogonas
Date: 2024-03-20 00:00:00

In this paper, we present a novel 3D formation control scheme for directed graphs in a leader-follower configuration, achieving (almost) global convergence to the desired shape. Specifically, we introduce three controlled variables representing bispherical coordinates that uniquely describe the formation in 3D. Acyclic triangulated directed graphs (a class of minimally acyclic persistent graphs) are used to model the inter-agent sensing topology, while the agents' dynamics are governed by single-integrator model. Our analysis demonstrates that the proposed decentralized formation controller ensures (almost) global asymptotic stability while avoiding potential shape ambiguities in the final formation. Furthermore, the control laws are implementable in arbitrarily oriented local coordinate frames of follower agents using only low-cost onboard vision sensors, making it suitable for practical applications. Finally, we validate our formation control approach by a simulation study.
Bayesian Physics-informed Neural Networks for System Identification of Inverter-dominated Power Systems
Authors: Simon Stock, Davood Babazadeh, Christian Becker, Spyros Chatzivasileiadis
Date: 2024-03-20 00:00:00
Tasks: Transfer Learning, Uncertainty Quantification

While the uncertainty in generation and demand increases, accurately estimating the dynamic characteristics of power systems becomes crucial for employing the appropriate control actions to maintain their stability. In our previous work, we have shown that Bayesian Physics-informed Neural Networks (BPINNs) outperform conventional system identification methods in identifying the power system dynamic behavior under measurement noise. This paper takes the next natural step and addresses the more significant challenge, exploring how BPINN perform in estimating power system dynamics under increasing uncertainty from many Inverter-based Resources (IBRs) connected to the grid. These introduce a different type of uncertainty, compared to noisy measurements. The BPINN combines the advantages of Physics-informed Neural Networks (PINNs), such as inverse problem applicability, with Bayesian approaches for uncertainty quantification. We explore the BPINN performance on a wide range of systems, starting from a single machine infinite bus (SMIB) system and 3-bus system to extract important insights, to the 14-bus CIGRE distribution grid, and the large IEEE 118-bus system. We also investigate approaches that can accelerate the BPINN training, such as pretraining and transfer learning. Throughout this paper, we show that in presence of uncertainty, the BPINN achieves orders of magnitude lower errors than the widely popular method for system identification SINDy and significantly lower errors than PINN, while transfer learning helps reduce training time by up to 80 %.
Lattice piecewise affine approximation of explicit model predictive control with application to satellite attitude control
Authors: Zhengqi Xu, Jun Xu, Ai-Guo Wu, Shuning Wang
Date: 2024-03-20 00:00:00
Tasks: Model Predictive Control

Satellite attitude cotrol is a crucial part of aerospace technology, and model predictive control(MPC) is one of the most promising controllers in this area, which will be less effective if real-time online optimization can not be achieved. Explicit MPC converts the online calculation into a table lookup process, however the solution is difficult to obtain if the system dimension is high or the constraints are complex. The lattice piecewise affine(PWA) function was used to represent the control law of explicit MPC, although the online calculation complexity is reduced, the offline calculation is still prohibitive for complex problems. In this paper, we use the sample points in the feasible region with their corresponding affine functions to construct the lattice PWA approximation of the optimal MPC controller designed for satellite attitude control. The asymptotic stability of satellite attitude control system under lattice PWA approximation has been proven, and simulations are executed to verify that the proposed method can achieve almost the same performance as linear online MPC with much lower online computational complexity and use less fuel than LQR method.
Augmented LRFS-based Filter: Holistic Tracking of Group Objects
Authors: Chaoqun Yang, Xiaowei Liang, Zhiguo Shi, Heng Zhang, Xianghui Cao
Date: 2024-03-20 00:00:00

This paper addresses the problem of group target tracking (GTT), wherein multiple closely spaced targets within a group pose a coordinated motion. To improve the tracking performance, the labeled random finite sets (LRFSs) theory is adopted, and this paper develops a new kind of LRFSs, i.e., augmented LRFSs, which introduces group information into the definition of LRFSs. Specifically, for each element in an LRFS, the kinetic states, track label, and the corresponding group information of its represented target are incorporated. Furthermore, by means of the labeled multi-Bernoulli (LMB) filter with the proposed augmented LRFSs, the group structure is iteratively propagated and updated during the tracking process, which achieves the simultaneously estimation of the kinetic states, track label, and the corresponding group information of multiple group targets, and further improves the GTT tracking performance. Finally, simulation experiments are provided, which well demonstrates the effectiveness of the labeled multi-Bernoulli filter with the proposed augmented LRFSs for GTT tracking.
An Extended Kuramoto Model for Frequency and Phase Synchronization in Delay-Free Networks with Finite Number of Agents
Authors: Andreas Bathelt, Vimukthi Herath, Thomas Dallmann
Date: 2024-03-20 00:00:00

Due to its description of a synchronization between oscillators, the Kuramoto model is an ideal choice for a synchronisation algorithm in networked systems. This requires to achieve not only a frequency synchronization but also a phase synchronization - something the standard Kuramoto model can not provide for a finite number of agents. In this case, a remaining phase difference is necessary to offset differences of the natural frequencies. Setting the Kuramoto model into the context of dynamic consensus and making use of the $n$th order discrete average consensus algorithm, this paper extends the standard Kuramoto model in such a way that frequency and phase synchronization are separated. This in turn leads to an algorithm achieve the required frequency and phase synchronization also for a finite number of agents. Simulations show the viability of this extended Kuramoto model.
Charged Momentum: Electric Vehicle Surge in India's 2023 Landscape
Authors: Rahul Wagh
Date: 2024-03-20 00:00:00

Electric vehicles (EVs) have emerged as a transformative force in India's transportation sector, offering a sustainable solution to the country's growing energy and environmental challenges. Against the backdrop of rapid urbanization, rising pollution levels, and the need for energy security, EVs have gained traction as a viable alternative to traditional internal combustion engine vehicles. This paper provides a comprehensive analysis of the electric vehicle market in India, focusing particularly on the landscape of 2023. It emphasizes key aspects such as the 2023 scenario of EV adoption, the role of indigenous manufacturers, dominant players shaping the market, and the influence of government policies and initiatives, including the FAME I and II schemes. Furthermore, the paper delves into EV sales data for the fiscal year 2023, offering insights into market trends and consumer preferences. By elucidating the current state of EVs in India, this paper aims to contribute to a deeper understanding of the country's transition towards sustainable mobility and its implications for energy, environment, and economy.
Quantifying the Aggregate Flexibility of Electric Vehicle Charging Stations for Market-based Congestion Management Services
Authors: Nanda Kishor Panda, Simon H. Tindemans
Date: 2024-03-20 00:00:00
Tasks: Management

Electric vehicles (EVs) play a crucial role in the transition towards sustainable modes of transportation and thus are critical to the energy transition. As their number grows, managing the aggregate power of EV charging is crucial to maintain grid stability and mitigate congestion. This study analyses more than 500 thousand real charging transactions in the Netherlands to explore the challenge and opportunity for the energy system presented by EV growth and smart charging flexibility. Specifically, it analyses the collective ability to provide congestion management services according to the specifications of those services in the Netherlands. In this study, a data-driven model of charging behaviour is created to explore the implications of delivering dependable congestion management services at various aggregation levels and types of service. The probabilistic ability to offer different flexibility products, namely, redispatch and capacity limitation, for congestion management, is assessed for different categories of charging stations (CS) and dispatch strategies. These probabilities can help EV aggregators, such as charging point operators, make informed decisions about offering congestion mitigation products per relevant regulations and distribution system operators to assess their potential. Further, it is shown how machine learning models can be incorporated to predict the day-ahead consumption, followed by operationally predicting redispatch flexibility. The findings demonstrate that the timing of EV arrivals, departures, and connections plays a crucial role in determining the feasibility of product offerings, and dependable services can generally be delivered using a sufficiently large number of CSs.
A Control-Recoverable Added-Noise-based Privacy Scheme for LQ Control in Networked Control Systems
Authors: Xuening Tang, Xianghui Cao, Wei Xing Zheng
Date: 2024-03-20 00:00:00
Tasks: Privacy Preserving, State Estimation

As networked control systems continue to evolve, ensuring the privacy of sensitive data becomes an increasingly pressing concern, especially in situations where the controller is physically separated from the plant. In this paper, we propose a secure control scheme for computing linear quadratic control in a networked control system utilizing two networked controllers, a privacy encoder and a control restorer. Specifically, the encoder generates two state signals blurred with random noise and sends them to the controllers, while the restorer reconstructs the correct control signal. The proposed design effectively preserves the privacy of the control system's state without sacrificing the control performance. We theoretically quantify the privacy-preserving performance in terms of the state estimation error of the controllers and the disclosure probability. Moreover, we extend the proposed privacy-preserving scheme and evaluation method to cases where collusion between two controllers occurs. Finally, we verify the validity of our proposed scheme through simulations.