pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Control of Renewable Energy Communities subject to Network Peak Fees with Model Predictive Control and Reinforcement Learning Algorithms
Authors: Samy Aittahar, Adrien Bolland, Guillaume Derval, Damien Ernst
Date: 2024-01-29 00:00:00
Tasks: Model Predictive Control, reinforcement-learning

We propose in this paper an optimal control framework for renewable energy communities (RECs) equipped with controllable assets. Such RECs allow its members to exchange production surplus through an internal market. The objective is to control their assets in order to minimise the sum of individual electricity bills. These bills account for the electricity exchanged through the REC and with the retailers. Typically, for large companies, another important part of the bills are the costs related to the power peaks; in our framework, they are determined from the energy exchanges with the retailers. We compare rule-based control strategies with the two following control algorithms. The first one is derived from model predictive control techniques, and the second one is built with reinforcement learning techniques. We also compare variants of these algorithms that neglect the peak power costs. Results confirm that using policies accounting for the power peaks lead to a significantly lower sum of electricity bills and thus better control strategies at the cost of higher computation time. Furthermore, policies trained with reinforcement learning approaches appear promising for real-time control of the communities, where model predictive control policies may be computationally expensive in practice. These findings encourage pursuing the efforts toward development of scalable control algorithms, operating from a centralised standpoint, for renewable energy communities equipped with controllable assets.
Scalable Reinforcement Learning for Linear-Quadratic Control of Networks
Authors: Johan Olsson, Runyu Zhang, Emma Tegling, Na Li
Date: 2024-01-29 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

Distributed optimal control is known to be challenging and can become intractable even for linear-quadratic regulator problems. In this work, we study a special class of such problems where distributed state feedback controllers can give near-optimal performance. More specifically, we consider networked linear-quadratic controllers with decoupled costs and spatially exponentially decaying dynamics. We aim to exploit the structure in the problem to design a scalable reinforcement learning algorithm for learning a distributed controller. Recent work has shown that the optimal controller can be well approximated only using information from a $\kappa$-neighborhood of each agent. Motivated by these results, we show that similar results hold for the agents' individual value and Q-functions. We continue by designing an algorithm, based on the actor-critic framework, to learn distributed controllers only using local information. Specifically, the Q-function is estimated by modifying the Least Squares Temporal Difference for Q-functions method to only use local information. The algorithm then updates the policy using gradient descent. Finally, we evaluate the algorithm through simulations that indeed suggest near-optimal performance.
Multi-BD Symbiotic Radio-Aided 6G IoT Network: Energy Consumption Optimization with QoS Constraint Approach
Authors: Rahman Saadat Yeganeh, Mohammad Javad Omidi, Mohammad Ghavami
Date: 2024-01-29 00:00:00
Tasks: Scheduling

The commensal symbiotic radio (CSR) system is proposed as a novel solution for connecting systems through green communication networks. This system enables us to establish secure, ubiquitous, and unlimited connectivity, which is a goal of 6G. The base station uses MIMO antennas to transmit its signal. Passive IoT devices, called symbiotic backscatter devices (SBDs), receive the signal and use it to charge their power supply. When the SBDs have data to transmit, they modulate the information onto the received ambient RF signal and send it to the symbiotic user equipment, which is a typical active device. The main purpose is to enhance energy efficiency in this network by minimizing energy consumption (EC) while ensuring the minimum required throughput for SBDs. To achieve this, we propose a new scheduling scheme called Timing-SR that optimally allocates resources to SBDs. The main optimization problem involves non-convex objective functions and constraints. To solve this, we use mathematical techniques and introduce a new approach called sequential quadratic and conic quadratic representation to relax and discipline the problem, leading to reducing its complexity and convergence time. The simulation results demonstrate that the proposed approach outperforms other outlined schemes in reducing EC.
Model predictive control of wakes for wind farm power tracking
Authors: Arnold Sterle, Christian A. Hans, Jörg Raisch
Date: 2024-01-29 00:00:00
Tasks: Model Predictive Control

In this paper, a model predictive control scheme for wind farms is presented. Our approach considers wake dynamics including their influence on local wind conditions and allows to track a given power reference. In detail, a Gaussian wake model is used in combination with observation points that carry wind condition information. This allows to estimate the rotor effective wind speeds at downstream turbines based on which we deduce their power output. Through different approximation methods, the associated finite horizon nonlinear optimization problem is reformulated in a mixed-integer quadratically-constrained quadratic program fashion. By solving the reformulated problem online, optimal yaw angles and axial induction factors are found. Closed-loop simulations indicate good power tracking capabilities over a wide range of power setpoints while distributing wind turbine infeed evenly among all units. Additionally, the simulation results underline real time capabilities of our approach.
A Novel Geometric Solution for Moving Target Localization through Multistatic Sensing in the ISAC System
Authors: S. Zhuge, Y. Ma, Z. Lin, Y. Zeng
Date: 2024-01-29 00:00:00
Tasks: Integrated sensing and communication, ISAC

This paper proposes a novel geometric solution for tracking a moving target through multistatic sensing. In contrast to existing two-step weighted least square (2SWLS) methods which use the bistatic range (BR) and bistatic range rate (BRR) measurements, the proposed method incorporates an additional direction of arrival (DOA) measurement of the target obtained from a communication receiver in an integrated sensing and communication (ISAC) system. Unlike the existing 2SWLS methods that require at least three transmitter-receiver (TX-RX) pairs to operate, the proposed algorithm can conduct location estimation with a single TX-RX pair and velocity estimation with two TX-RX pairs. Simulations reveal that the proposed method exhibits superior performance compared to existing 2SWLS methods, particularly when dealing with moderate levels of noise in DOA measurements.
Opinion Dynamics in Social Multiplex Networks with Mono and Bi-directional Interactions in the Presence of Leaders
Authors: Amirreza Talebi, Sayed Pedram Haeri Boroujeni, Abolfazl Razi
Date: 2024-01-29 00:00:00

We delve into the dynamics of opinions within a multiplex network using coordination games, where agents communicate either in a one-way or two-way interactions, and where a designated leader may be present. By employing graph theory and Markov chains, we illustrate that despite non-positive diagonal elements in transition probability matrices or decomposable layers, opinions generally converge under specific conditions, leading to a consensus. We further scrutinize the convergence rates of opinion dynamics in networks with one-way versus two-way interactions. We find that in networks with a designated leader, opinions converge towards the initial opinion of the leader, whereas in networks without a designated leader, opinions converge to a convex combination of the opinions of agents. Moreover, we emphasize the crucial role of designated leaders in steering opinion convergence within the network. Our experimental findings corroborate that the presence of leaders expedites convergence, with mono-directional interactions exhibiting notably faster convergence rates compared to bidirectional interactions.
Deep Reinforcement Learning for Voltage Control and Renewable Accommodation Using Spatial-Temporal Graph Information
Authors: Jinhao Li, Ruichang Zhang, Hao Wang, Zhi Liu, Hongyang Lai, Yanru Zhang
Date: 2024-01-29 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning

Renewable energy resources (RERs) have been increasingly integrated into distribution networks (DNs) for decarbonization. However, the variable nature of RERs introduces uncertainties to DNs, frequently resulting in voltage fluctuations that threaten system security and hamper the further adoption of RERs. To incentivize more RER penetration, we propose a deep reinforcement learning (DRL)-based strategy to dynamically balance the trade-off between voltage fluctuation control and renewable accommodation. To further extract multi-time-scale spatial-temporal (ST) graphical information of a DN, our strategy draws on a multi-grained attention-based spatial-temporal graph convolution network (MG-ASTGCN), consisting of ST attention mechanism and ST convolution to explore the node correlations in the spatial and temporal views. The continuous decision-making process of balancing such a trade-off can be modeled as a Markov decision process optimized by the deep deterministic policy gradient (DDPG) algorithm with the help of the derived ST information. We validate our strategy on the modified IEEE 33, 69, and 118-bus radial distribution systems, with outcomes significantly outperforming the optimization-based benchmarks. Simulations also reveal that our developed MG-ASTGCN can to a great extent accelerate the convergence speed of DDPG and improve its performance in stabilizing node voltage in an RER-rich DN. Moreover, our method improves the DN's robustness in the presence of generator failures.
Cross-Layer Performance Evaluation of C-V2X
Authors: Dhruba Sunuwar, Seungmo Kim
Date: 2024-01-29 00:00:00
Tasks: Self-Driving Cars

As self-driving cars increasingly penetrate our daily lives, vehicle-to-everything (V2X) communications are emerging as one of the key enabler technologies. However, the dynamicity of vehicles (one of whose causes is the mobility of vehicles) often complicates it even further to evaluate the performance of a V2X system. We have been building a system-level simulator dedicated to assessing the performance of V2X communications. We highlight that the simulator features the incorporation of (i) intelligent transportation system (ITS) scenarios in geographical setup and (ii) physical (PHY) and radio resource control (RRC) cross-layer performance evaluation capability. In particular, this abstract reports the status of our implementation of the modulation and coding scheme (MCS).
Innovation-triggered Learning with Application to Data-driven Predictive Control
Authors: Kaikai Zheng, Dawei Shi, Sandra Hirche, Yang Shi
Date: 2024-01-29 00:00:00

Data-driven control has attracted lots of attention in recent years, especially for plants that are difficult to model based on first principles. In particular, a key issue in data-driven approaches is how to make efficient use of data as the abundance of data becomes overwhelming. To address this issue, this work proposes an innovation-triggered learning framework and a corresponding data-driven controller design approach with guaranteed stability. Specifically, we consider a linear time-invariant system with unknown dynamics. A set-membership approach is introduced to learn a parametric uncertainty set for the unknown dynamics. Then, a data selection mechanism is proposed by online evaluating the innovation contained in the sampled data, wherein the innovation is quantified by its effect of shrinking the parametric uncertainty set. Next, after introducing a stability criterion using the set-membership estimate of the system dynamics, a robust data-driven predictive controller is designed by minimizing a worst-case cost function. The closed-loop stability of the data-driven predictive controller equipped with the innovation-triggered learning protocol is discussed within a high probability framework. Finally, comparative numerical experiments are performed to verify the validity of the proposed approach, and the characteristics and the design principle of the learning hyper-parameter are also discussed.
Power based adaptive compensator of output oscillations
Authors: Michael Ruderman
Date: 2024-01-28 00:00:00

Power-based output feedback compensator for oscillatory systems is proposed. The average input-output power of an oscillatory signal serves as an equivalent control effort, while the unknown amplitude and frequency of oscillations are detected at each half-period. This makes the compensator adaptive and discrete, while the measured oscillatory output is the single available signal in use. The resulting discrete control scheme enables a drastic reduction of communication efforts in the control loop. The compensator is designed for 2nd order systems, while an extension to higher-order dynamics, like e.g. in case of two-inertia systems, is also provided. Illustrative experimental case study of the 5th order oscillatory system is provided.
Framework of Resilient Transmission Network Reconfiguration Considering Cyber-Attacks
Authors: Chao Yang, Gaoqi Liang, Steven R. Weller, Shaoyan Li, Junhua Zhao, ZhaoYang Dong
Date: 2024-01-28 00:00:00

Fast and reliable transmission network reconfiguration is critical in improving power grid resilience to cyber-attacks. If the network reconfiguration following cyber-attacks is imperfect, secondary incidents may delay or interrupt post-attack restoration of the power grid. This paper proposes a framework of resilient transmission network reconfiguration, taking into account the impacts of cyber-attacks in the network reconfiguration process. First, the mechanism of cyber-attack propagation is analyzed based on the characteristics of network reconfiguration. Second, systematic resilience indices are specially extracted in which the impact of cyber-attacks on network reconfiguration is quantified. These indices are defined in terms of the restoration characteristics of the transmission power system. Third, representative cyber-attack incidents motivate an optimization-based model of resilient transmission network reconfiguration, and an optimal reconstruction scheme is obtained. Finally, simulation results based on the IEEE 39-bus system verify the feasibility and effectiveness of the proposed framework in enhancing power grid resilience to cyber-attacks.
Pulse-Width Modulation Technique With Harmonic Injection in the Modulating Wave and Discontinuous Frequency Modulation for the Carrier Wave for Multilevel Inverters: An Application to the Reduction of Acoustic Noise in Induction Motors
Authors: Antonio Ruiz-Gonzalez, Juan-Ramon Heredia-Larrubia, Mario J. Meco-Gutierrez, Francisco-M. Perez-Hidalgo
Date: 2024-01-27 00:00:00

An implementation of a harmonic injection pulse width modulation frequency-modulated triangular carrier (HIPWM-FMTC) control strategy applied to a multilevel power inverter feeding an asynchronous motor is presented. The aim was to justify the reduction in acoustic noise emitted by the machine compared with other strategies in the technical literature. In addition, we checked how the THD at the inverter output was reduced compared to the other control techniques used as a reference. The proposed strategy is based on frequency modulation of the triangular carrier. The main advantage of the proposed method is that only one control parameter is required for modifying the electrical spectrum. Therefore, the mechanical natural frequencies and spatial harmonics of the machine can be avoided, and acoustic noise can be reduced. The effectiveness of the technique was demonstrated after laboratory validation by comparing the acoustic results for a 1 kW motor. The results obtained from the laboratory tests are presented and compared with those of other acoustic measurements using different PWM strategies.
Epidemic Population Games And Perturbed Best Response Dynamics
Authors: Shinkyu Park, Jair Certorio, Nuno C. Martins, Richard J. La
Date: 2024-01-27 00:00:00

This paper proposes an approach to mitigate epidemic spread in a population of strategic agents by encouraging safer behaviors through carefully designed rewards. These rewards, which adapt to the evolving state of the epidemic, are ascribed by a dynamic payoff mechanism we seek to design. We use a modified SIRS model to track how the epidemic progresses in response to the agents' strategic choices. By employing perturbed best response evolutionary dynamics to model the population's strategic behavior, we extend previous related work so as to allow for noise in the agents' perceptions of the rewards and intrinsic costs of the available strategies. Central to our approach is the use of system-theoretic methods and passivity concepts to obtain a Lyapunov function, ensuring the global asymptotic stability of an endemic equilibrium with minimized infection prevalence under budget constraints. We leverage the Lyapunov function to analyze how the epidemic's spread rate is influenced by the time scale of the payoff mechanism's dynamics. Additionally, we derive anytime upper bounds on both the infectious fraction of the population and the instantaneous cost a social planner must incur to control the spread, allowing us to quantify the trade-off between peak infection prevalence and the corresponding cost. For a class of one-parameter perturbed best response models, we propose a method to learn the model's parameter from data.
An overview of IoT architectures, technologies, and existing open-source projects
Authors: Tomás Domínguez-Bolaño, Omar Campos, Valentín Barral, Carlos J. Escudero, José A. García-Naya
Date: 2024-01-27 00:00:00

Today's needs for monitoring and control of different devices in organizations require an Internet of Things (IoT) platform that can integrate heterogeneous elements provided by multiple vendors and using different protocols, data formats and communication technologies. This article provides a comprehensive review of all the architectures, technologies, protocols and data formats most commonly used by existing IoT platforms. On this basis, a comparative analysis of the most widely used open source IoT platforms is presented. This exhaustive comparison is based on multiple characteristics that will be essential to select the platform that best suits the needs of each organization.
Online Data-Driven Adaptive Control for Unknown Linear Time-Varying Systems
Authors: Shenyu Liu, Kaiwen Chen, Jaap Eising
Date: 2024-01-27 00:00:00

This paper proposes a novel online data-driven adaptive control for unknown linear time-varying systems. Initialized with an empirical feedback gain, the algorithm periodically updates this gain based on the data collected over a short time window before each update. Meanwhile, the stability of the closed-loop system is analyzed in detail, which shows that under some mild assumptions, the proposed online data-driven adaptive control scheme can guarantee practical global exponential stability. Finally, the proposed algorithm is demonstrated by numerical simulations and its performance is compared with other control algorithms for unknown linear time-varying systems.
Quantum-Assisted Adaptive Beamforming in UASs Network: Enhancing Airborne Communication via Collaborative UASs for NextG IoT
Authors: Sudhanshu Arya, Ying Wang
Date: 2024-01-26 00:00:00

This paper introduces a novel quantum-based method for dynamic beamforming and re-forming in Unmanned Aircraft Systems (UASs), specifically addressing the critical challenges posed by the unavoidable hovering characteristics of UAVs. Hovering creates significant beam path distortions, impacting the reliability and quality of distributed beamforming in airborne networks. To overcome these challenges, our Quantum Search for UAS Beamforming (QSUB) employs quantum superposition, entanglement, and amplitude amplification. It adaptively reconfigures beams, enhancing beam quality and maintaining robust communication links in the face of rapid UAS state changes due to hovering. Furthermore, we propose an optimized framework, Quantum-Position-Locked Loop (Q-P-LL), that is based on the principle of the Nelder-Mead optimization method for adaptive search to reduce prediction error and improve resilience against angle-of-arrival estimation errors, crucial under dynamic hovering conditions. We also demonstrate the scalability of the system performance and computation complexity by comparing various numbers of active UASs. Importantly, QSUB and Q-P-LL can be applied to both classical and quantum computing architectures. Comparative analyses with conventional Maximum Ratio Transmission (MRT) schemes demonstrate the superior performance and scalability of our quantum approaches, marking significant advancements in the next-generation Internet of Things (IoT) applications requiring reliable airborne communication networks.
Backscatter Measurements and Models for RF Sensing Applications in Cluttered Environments
Authors: Dmitry Chizhik, Jinfeng Du, Jakub Sapis, Reinaldo A. Valenzuela, Abhishek Adhikari, Gil Zussman, Manuel A. Almendra, Mauricio Rodriguez, Rodolfo Feick
Date: 2024-01-26 00:00:00

A statistical backscatter channel model for indoor clutter is developed for indoor RF sensing applications based on measurements. A narrowband 28 GHz sounder used a quazi-monostatic radar arrangement with an omnidirectional transmit antenna illuminating an indoor scene and a spinning horn receive antenna less than 1 m away collecting backscattered power as a function of azimuth. Median average backscatter power was found to vary over a 12 dB range, with average power generally decreasing with increasing room size. A deterministic model of average backscattered power dependent on distance to nearest wall and wall reflection coefficient reproduces observations with 4.0 dB RMS error. Distribution of power variation in azimuth around this average is reproduced within 1 dB by a random azimuth spectrum with a lognormal amplitude distribution and uniformly random phase. The model is extended to provide power distribution over both azimuth and delay (conveying range to scatterer) by combining azimuthal distribution with published results on power delay profiles in reverberant environments. The statistical model does not require a detailed room layout description, aiming to reproduce backscatter clutter statistics, as opposed to a deterministic response.
Overview of Sensing Attacks on Autonomous Vehicle Technologies and Impact on Traffic Flow
Authors: Zihao Li, Sixu Li, Hao Zhang, Yang Zhou, Siyang Xie, Yunlong Zhang
Date: 2024-01-26 00:00:00
Tasks: Autonomous Vehicles

While perception systems in Connected and Autonomous Vehicles (CAVs), which encompass both communication technologies and advanced sensors, promise to significantly reduce human driving errors, they also expose CAVs to various cyberattacks. These include both communication and sensing attacks, which potentially jeopardize not only individual vehicles but also overall traffic safety and efficiency. While much research has focused on communication attacks, sensing attacks, which are equally critical, have garnered less attention. To address this gap, this study offers a comprehensive review of potential sensing attacks and their impact on target vehicles, focusing on commonly deployed sensors in CAVs such as cameras, LiDAR, Radar, ultrasonic sensors, and GPS. Based on this review, we discuss the feasibility of integrating hardware-in-the-loop experiments with microscopic traffic simulations. We also design baseline scenarios to analyze the macro-level impact of sensing attacks on traffic flow. This study aims to bridge the research gap between individual vehicle sensing attacks and broader macroscopic impacts, thereby laying the foundation for future systemic understanding and mitigation.