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Multi-Objective Coordinated Ev Charging Strategy in Distribution Networks Using an Improved Augmented Epsilon-Constrained Method

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SSRN

SSRN 2024年

作者： Wang, Yunqi Wang, Hao Razzaghi, Reza Jalili, Mahdi Liebman, Ariel Department of Data Science and AI Monash University ClaytonVIC3800 Australia Monash Energy Institute Monash University ClaytonVIC3800 Australia Department of Electrical and Computer Systems Engineering Monash University ClaytonVIC3800 Australia MelbourneVIC3000 Australia The University of Sydney Australia

The surging adoption of electric vehicles (EVs) poses significant challenges for distribution networks (DNs) due to EV charging impact. This paper presents a multi-objective optimization (MOO) model that coordinates EV charging in DNs, aiming to address the interests of different stakeholders, such as the distribution network operator (DNO) and EV owners and achieve a balanced outcome. Specifically, our model’s objectives include minimizing the operation costs for distribution network operators (DNO), power loss in the DN, and EV owners’ charging expenses, emphasizing the delicate trade-off between these objectives. An innovative improved-augmented epsilon-constrained (IAUGMENCON)method is proposed to tackle the complex trade-offs by solving the MOO effectively and efficiently. A case study on an modified IEEE 33-bus DN attests to the strategy’s efficacy, showcasing a range of solutions that coordinate DNO’s costs, DN power loss, and EV charging costs. Furthermore, our I-AUGMENCON outperforms other prevalent MOO solution methods, such as the weighted-sum and NSGA-II, in determining non-dominated solutions and obtaining a Pareto-efficient solution set for the MOO to characterize an effective trade-off between three key objectives. © 2024, The Authors. All rights reserved.

关键词： Electric power distribution

Towards Energy-Aware Federated Traffic Prediction for Cellular Networks

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Towards Energy-Aware Federated Traffic Prediction for Cellul...

International Conference on Fog and Mobile Edge Computing (FMEC)

作者： Vasileios Perifanis Nikolaos Pavlidis Selim F. Yilmaz Francesc Wilhelmi Elia Guerra Marco Miozzo Pavlos S. Efraimidis Paolo Dini Remous-Aris Koutsiamanis Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece Department of Electrical and Electronic Engineering Imperial College London London United Kingdom Radio Systems Research Nokia Bell Labs Stuttgart Germany Sustainable Artificial Intelligence Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA) Barcelona Spain Department of Automation Production and Computer Sciences LS2N IMT Atlantique Inria Nantes France

Cellular traffic prediction is a crucial activity for optimizing networks in fifth-generation (5G) networks and beyond, as accurate forecasting is essential for intelligent network design, resource allocation and anomaly mitigation. Although machine learning (ML) is a promising approach to effectively predict network traffic, the centralization of massive data in a single data center raises issues regarding confidentiality, privacy and data transfer demands. To address these challenges, federated learning (FL) emerges as an appealing ML training framework which offers high accurate predictions through parallel distributed computations. However, the environmental impact of these methods is often overlooked, which calls into question their sustainability. In this paper, we address the trade-off between accuracy and energy consumption in FL by proposing a novel sustainability indicator that allows assessing the feasibility of ML models. Then, we comprehensively evaluate state-of-the-art deep learning (DL) architectures in a federated scenario using real-world measurements from base station (BS) sites in the area of Barcelona, Spain. Our findings indicate that larger ML models achieve marginally improved performance but have a significant environmental impact in terms of carbon footprint, which make them impractical for real-world applications.

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PRIMAL-DUAL FIRST-ORDER METHODS FOR AFFINELY CONSTRAINED MULTI-BLOCK SADDLE POINT PROBLEMS

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arXiv 2021年

作者： Zhang, Junyu Wang, Mengdi Hong, Mingyi Zhang, Shuzhong Department of Industrial Systems Engineering and Management National University of Singapore Singapore Department of Electrical and Computer Engineering Princeton University United States Department of Electrical and Computer Engineering University of Minnesota United States Department of Industrial and Systems Engineering University of Minnesota United States

We consider the convex-concave saddle point problem minx maxy Φ(x, y), where the decision variables x and/or y are subject to certain multi-block structure and affine coupling constraints, and Φ(x, y) possesses certain separable structure. Although the minimization counterpart of this problem has been widely studied under the topics of ADMM, this minimax problem is rarely investigated. In this paper, a convenient notion of ∈-saddle point is proposed, under which the convergence rate of several proposed algorithms are analyzed. When only one of x and y has multiple blocks and affine constraint, several natural extensions of ADMM are proposed to solve the problem. Depending on the number of blocks and the level of smoothness, O(1/T) or O(1/√T) convergence rates are derived for our algorithms. When both x and y have multiple blocks and affine constraints, a new algorithm called Extra-Gradient Method of Multipliers (EGMM) is proposed. Under desirable smoothness conditions, an O(1/T) rate of convergence can be guaranteed regardless of the number of blocks in x and y. An in-depth comparison between EGMM (fully primal-dual method) and ADMM (approximate dual method) is made over the multi-block optimization problems to illustrate the advantage of the EGMM. Copyright © 2021, The Authors. All rights reserved.

关键词： Gradient methods

Optimal Active Fault Detection in Inverter-Based Grids

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arXiv 2022年

作者： Pirani, Mohammad Hosseinzadeh, Mehdi Taylor, Joshua A. Sinopoli, Bruno The Department of Electrical and Computer Engineering University of Toronto Canada The School of Mechanical and Materials Engineering Washington State University PullmanWA99164 United States The Department of Electrical and Systems Engineering Washington University in St Louis St. LouisMO63130 United States

Ground faults in converter-based grids can be difficult to detect because, unlike in grids with synchronous machines, they often do not result in large currents. One recent strategy is for each converter to inject a perturbation that makes faults easier to distinguish from normal operation. In this paper, we construct optimal perturbation sequences for use with the Multiple Model Kalman Filter. The perturbations maximize the difference between faulty and fault-free operation while respecting limits on performance degradation. Simulations show that the optimal input sequence increases the confidence of fault detection while decreasing detection time. It is shown that there is a tradeoff between detection and degradation of the control performance, and that the method is robust to parameter variations. © 2022, CC BY.

关键词： Fault detection

Pattern Recognition Method for Detecting engineering Errors on Technical Drawings

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Pattern Recognition Method for Detecting Engineering Errors ...

AI IoT Congress (AIIoT), World

作者： Rimma Dzhusupova Richa Banotra Jan Bosch Helena Holmströ m Olsson Department of Electrical &#x0026 Instrumentation Control &#x0026 Safety Systems McDermott The Hague The Netherlands Department of Instrumentation Control &#x0026 Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Department of Computer Science and Media Technology Malm&#x00F6 University Malm&#x00F6 Sweden

Many organizations are looking for how to automate repetitive tasks to reduce manual work and free up resources for innovation. Machine Learning, especially Deep Learning, increases the chance of achieving this goal while working with technical documentation. Highly costly engineering hours can be saved, for example, by empowering the manual check with AI, which helps to reduce the total time for technical documents review. This paper proposes a way to substantially reduce the hours spent by process engineers reviewing P&IDs (Piping & Instrumentation Diagrams). The developed solution is based on a deep learning model for analyzing complex real-life engineering diagrams to find design errors - patterns that are combinations of high-level objects. Through the research on an extensive collection of P&ID files provided by McDermott, we prove that our model recognizes patterns representing engineering mistakes with high accuracy. We also describe our experience dealing with class-imbalance problems, labelling, and model architecture selection. The developed model is domain agnostic and can be re-trained on various schematic diagrams within engineering fields and, as well, could be used as an idea for other researchers to see whether similar solutions could be built for different industries.

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An experimental scheme for determining the Berry phase in two-dimensional quantum materials with a flat band

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arXiv 2024年

作者： Ye, Li-Li Wang, Cheng-Zhen Lai, Ying-Cheng School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Wave Transport in Complex Systems Lab Department of Physics Wesleyan University MiddletownCT06459 United States Department of Physics Arizona State University TempeAZ85287 United States

Experimentally feasible methods to determine the Berry phase, a fundamental quantity characterizing a quantum material, are often needed in applications. We develop an approach to detecting the Berry phase by using a class of two-dimensional (2D) Dirac materials with a flat band, the α-T3 lattices. The properties of this class of quantum materials are controlled by a single parameter 0 ≤ α ≤ 1, where the left and right endpoints correspond to graphene with pseudospin-1/2 and the dice lattice with pseudospin-1 Dirac-Weyl quasiparticles, respectively, and each specific value of α represents a material with a unique Berry phase. Applying a constant electric field to the α-T3 lattice, we calculate the resulting electric current and find a one-to-one correspondence between the current and the Berry phase in both the linear and nonlinear response regimes. In the linear (Kubo) regime, the main physics is the Zitterbewegung effect. In the nonlinear regime, the Schwinger mechanism dominates. Beyond the nonlinear regime, Bloch-Zener oscillations can arise. Measuring the current thus provides an effective and experimentally feasible way to determine the Berry phase for this spectrum of 2D quantum materials. Copyright © 2024, The Authors. All rights reserved.

关键词： Nanocrystals

A vector almost-supermartingale convergence theorem and its applications

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A vector almost-supermartingale convergence theorem and its ...

IEEE Conference on Decision and Control

作者： Aditya Mahajan Silviu-Iulian Niculescu Mathukumalli Vidyasagar department of Electrical and Computer Engineering at McGill University Montreal QC Canada CNRS CentraleSupelec Inria Saclay Laboratory of Signals and Systems (L2S) University of Paris-Saclay Gif-sur-Yvette France department of Artificial Intelligence at the Indian Institute of Technology Hyderabad Kandi Telangana India

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

The almost-supermartingale convergence theorem of Robbins and Siegmund (1971) is a fundamental tool for establishing the convergence of various stochastic iterative algorithms including system identification, adaptive control, and reinforcement learning. The theorem is stated for non-negative scalar valued stochastic processes. In this paper, we generalize the theorem to non-negative vector valued stochastic processes and provide two set of sufficient conditions for such processes to converge almost surely. We present several applications of vector almost-supermartingale convergence theorem, including convergence of autoregressive supermartingales, delayed supermartingales, and stochastic approximation with delayed updates.

关键词： Sufficient conditions Temporal difference learning Stochastic processes Vectors System identification Iterative methods Adaptive control Convergence

Imaging Stars with Quantum Error Correction

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Physical Review Letters 2022年第21期129卷 210502-210502页

作者： Zixin Huang Gavin K. Brennen Yingkai Ouyang Centre for Engineered Quantum Systems School of Mathematical and Physical Sciences Macquarie University NSW 2109 Australia Department of Electrical and Computer Engineering National University of Singapore Singapore Centre of Quantum Technologies National University of Singapore Singapore

The development of high-resolution, large-baseline optical interferometers would revolutionize astronomical imaging. However, classical techniques are hindered by physical limitations including loss, noise, and the fact that the received light is generally quantum in nature. We show how to overcome these issues using quantum communication techniques. We present a general framework for using quantum error correction codes for protecting and imaging starlight received at distant telescope sites. In our scheme, the quantum state of light is coherently captured into a nonradiative atomic state via stimulated Raman adiabatic passage, which is then imprinted into a quantum error correction code. The code protects the signal during subsequent potentially noisy operations necessary to extract the image parameters. We show that even a small quantum error correction code can offer significant protection against noise. For large codes, we find noise thresholds below which the information can be preserved. Our scheme represents an application for near-term quantum devices that can increase imaging resolution beyond what is feasible using classical techniques.

关键词： Imaging & optical processing Optical interferometry Quantum error correction Quantum information architectures & platforms Quantum metrology Quantum sensing

Generalized Policy Improvement Algorithms with Theoretically Supported Sample Reuse

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arXiv 2022年

作者： Queeney, James Paschalidis, Ioannis Ch. Cassandras, Christos G. Mitsubishi Electric Research Laboratories CambridgeMA02139 United States Department of Electrical and Computer Engineering Division of Systems Engineering Boston University BostonMA02215 United States

We develop a new class of model-free deep reinforcement learning algorithms for data-driven, learning-based control. Our Generalized Policy Improvement algorithms combine the policy improvement guarantees of on-policy methods with the efficiency of sample reuse, addressing a trade-off between two important deployment requirements for real-world control: (i) practical performance guarantees and (ii) data efficiency. We demonstrate the benefits of this new class of algorithms through extensive experimental analysis on a broad range of simulated control tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Adversarial machine learning