Technical Sessions

Session DAC1

Machine Learning and Optimization in Power Systems

Conference
11:00 AM — 12:50 PM +08
Local
Oct 25 Tue, 11:00 PM — 12:50 AM EDT

Detecting Cyber Attacks in Smart Grids with Massive Unlabeled Sensing Data

Hanyu Zeng (National University of Singapore & Advanced Digital Siciences Center, Singapore); Zhen Wei Ng (Illinois at Singapore, Singapore); Pengfei Zhou (University of Pittsburgh, USA & Advanced Digital Sciences Center, Singapore); Xin Lou (Singapore Institute of Technology & Advanced Digital Sciences Center, Singapore); David Yau (Singapore University of Technology and Design, Singapore); Marianne Winslett (University of Illinois, USA)

0
Modern power grids are undergoing significant changes driven by information and communication technologies (ICTs), and evolving into smart grids with higher efficiency and lower operation cost. Using ICTs, however, comes with an inevitable side effect that makes the power system more vulnerable to cyber attacks. In this paper, we propose a self-supervised learning-based framework to detect and identify various types of cyber attacks. Different from existing approaches, the proposed framework does not rely on large amounts of labeled data but makes use of the massive unlabeled data in the wild which are easily accessible. Specifically, the proposed framework adopts the BERT model from the natural language processing domain and learns generalizable and effective representations from the massive unlabeled sensing data, which capture the distinctive patterns of different attacks. Using the learned representations, together with a very small amount of labeled data, we can train a task-specific classifier to detect various types of cyber attacks. Experiment results in a 3-area power grid system with 37 buses demonstrate the superior performance of our framework over existing approaches, especially when a very limited amount of labeled data is available.

Distributed Nonlinear State Estimation in Electric Power Systems using Graph Neural Networks

Ognjen Kundacina (University of Novi Sad, Serbia); Mirsad Cosovic (University of Sarajevo, Bosnia and Herzegovina); Dragiša Mišković and Dejan Vukobratović (University of Novi Sad, Serbia)

0
Nonlinear state estimation (SE), with the goal of estimating complex bus voltages based on all types of measurements available in the power system, is usually solved using the iterative Gauss-Newton method. The nonlinear SE presents some difficulties when considering inputs from both phasor measurement units and supervisory control and data acquisition system. These include numerical instabilities, convergence time depending on the starting point of the iterative method, and the quadratic computational complexity of a single iteration regarding the number of state variables. This paper introduces an original graph neural network based SE implementation over the augmented factor graph of the nonlinear power system SE, capable of incorporating measurements on both branches and buses, as well as both phasor and legacy measurements. The proposed regression model has linear computational complexity during the inference time once trained, with a possibility of distributed implementation. Since the method is noniterative and non-matrix-based, it is resilient to the problems that the Gauss-Newton solver is prone to. Aside from prediction accuracy on the test set, the proposed model demonstrates robustness when simulating cyber attacks and unobservable scenarios due to communication irregularities. In those cases, prediction errors are sustained locally, with no effect on the rest of the power system's results.

A Neural Combinatorial Optimization Algorithm for Unit Commitment in AC Power Systems

Shahab Bahrami, Christine Chen and Vincent W.S. Wong (University of British Columbia, Canada)

0
The unit commitment (UC) problem in AC power systems is a mixed-integer nonlinear optimization program with a running time that scales exponentially with the number of generators. This paper addresses the time complexity of solving the UC problem by developing a deep learning method that determines the generator on/off states using a transformer deep neural network (DNN), and subsequently solves an AC optimal power flow (OPF) problem to obtain the generator setpoints. To promote the feasibility of the binary solution, we apply a neural combinatorial optimization algorithm to train the DNN, while penalizing infeasible power flow solutions. Also, to guarantee the optimality of the generator setpoints, we transform the AC OPF problem into a semidefinite program (SDP). The proposed algorithm can obtain a near-optimal solution to the UC problem in polynomial running time. Simulations are performed for two IEEE test systems. When compared with three existing UC algorithms in the literature, our proposed algorithm can obtain a solution with at least 2.14% lower operation cost and lower running time. When compared with the MOSEK solver, our proposed algorithm can obtain a solution with at most 1.97% greater operation cost, but with a significantly lower running time.

Learning Cascading Failure Interactions by Deep Convolutional Generative Adversarial Network

Shuchen Huang and Junjian Qi (Stevens Institute of Technology, USA)

0
In this paper, a cascading failure interaction learning method is proposed for real utility outage data. For better revealing the structure, we reorganize the failure interaction matrix based on Louvain community detection. A deep convolutional generative adversarial network (DCGAN) based method is then proposed to learn the implicit features for failure propagation in the interaction matrix. A systematic method is further developed to evaluate the performance of the learning method on missing interaction recovery and new interaction discovery. The effectiveness of the proposed method is validated on the 14-year real utility outage data from Bonneville Power Administration.

Fast Graphical Learning Method for Parameter Estimation in Large-Scale Distribution Networks

Wenyu Wang and Nanpeng Yu (University of California, Riverside, USA); Yue Zhao (Stony Brook University, USA)

0
In distribution systems with growing distributed energy resources, accurate estimation of network parameters is crucial to feeder modeling, monitoring and management. Although existing state-of-the-art parameter estimation algorithms such as physics-informed graphical learning (GL) have accurate estimation, they can potentially suffer from scalability issues due to slow training in larger networks. In this paper, we propose an upgraded graphical learning method called fast graphical learning (FGL) to improve the computational efficiency and scalability while preserving the merits of GL. In FGL, we develop faster alternative algorithms to replace the fixed-point-iteration-based FORWARD and BACKWARD algorithms in GL. These alternative algorithms are based on fast power flow calculation of the current injection method and more efficient state initialization by the linearized power flow model. A comprehensive numerical study on IEEE test feeders and large-scale real-world distribution feeders shows that FGL improves the computational efficiency by as much as 60 times in larger distribution networks while attaining the accuracy of the state-of-art algorithms.

Session Chair and Room

I Safak Bayram (University of Strathclyde, United Kingdom) — Room TT2-3

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Session SP1

Cyber-Security for Distributed Energy Resources

Conference
11:00 AM — 12:50 PM +08
Local
Oct 25 Tue, 11:00 PM — 12:50 AM EDT

Real-Time Cyber-Physical Analysis of Distribution Systems Using Digital Twins

Jairo Giraldo, Mohammed Masum Siraj Khan and Masood Parvania (University of Utah, USA)

0
This paper introduces a novel framework for cyber-physical analysis of power distribution systems using a real-time digital twin. The proposed architecture utilizes a digital twin as a real-time reference model that replicates the complex behavior of a power distribution system in order to perform real-time cyber-physical analysis such as detection of potentially malicious data manipulations, verification of control actions before being applied to the physical system, and monitoring of the status of the power grid in locations where physical measurements are not available. The implementation in a hardware-in-the-loop (HIL) testbed is introduced for power distribution systems that integrate a variety of devices such as protection relays, distributed energy resources, and energy storage. Finally, results in a modified IEEE 13 node test feeder illustrate that the proposed structure is capable of detecting and mitigating cyberattacks, and also validate control commands before being executed.

Identification of Intraday False Data Injection Attack on DER Dispatch Signals

Jip Kim (KENTECH, Korea / Columbia University, USA); Siddharth Bhela (Siemens Technology, USA); James Anderson and Gil Zussman (Columbia University, USA)

1
The urgent need for the decarbonization of power girds has accelerated the integration of renewable energy. Concurrently the increasing distributed energy resources (DER) and advanced metering infrastructures (AMI) have transformed the power grids into a more sophisticated cyber-physical system with numerous communication devices. While these transitions provide economic and environmental value, they also impose increased risk of cyber attacks and operational challenges. This paper investigates the vulnerability of the power grids with high renewable penetration against an intraday false data injection (FDI) attack on DER dispatch signals and proposes a kernel support vector regression (SVR) based detection model as a countermeasure. The intraday FDI attack scenario and the detection model are demonstrated in a numerical experiment using the HCE 187-bus test system.

Vulnerability of Distributed Inverter VAR Control in PV Distributed Energy System

Bo Tu, Wen-Tai Li and Chau Yuen (Singapore University of Technology and Design, Singapore)

0
This work studies the potential vulnerability of distributed control schemes in smart grids. To this end, we consider an optimal inverter VAR control problem within a PV integrated distribution network. First, we formulate the centralized optimization problem considering the reactive power priority and further reformulate the problem into a distributed framework by an accelerated proximal projection method. The inverter controller can curtail the PV output of each user by clamping the reactive power. To illustrate the studied distributed control scheme that may be vulnerable due to the two-hop information communication pattern, we present a heuristic attack injecting false data during the information exchange. Then we analyze the attack impact on the update procedure of critical parameters. A case study with an eight-node test feeder demonstrates that adversaries can violate the constraints of distributed control scheme without being detected through simple attacks such as the proposed attack.

Ensemble and Transfer Adversarial Attack on Smart Grid Demand-Response Mechanisms

Guihai Zhang and Biplab Sikdar (National University of Singapore, Singapore)

0
Demand Response (DR) mechanisms aim to meet the balance of the power supply and demand in smart grid by modulating consumers' demand and adjusting electric price based on power consumption patterns and forecasts. Deep Learning (DL) networks have been proved to have better detection of False Data Injection (FDI) attacks in such DR system than traditional statistical methods. Adversarial Machine Learning (AML) attacks can generate finely perturbed data that are able to mislead or disrupt the normal performance of a DL network and therefore bypass the DL-based attack detection in DR systems. However, existing research on AML attack methods in DR systems require a substitute model to generate the adversarial data and rely on the transferabilities of the data to attack the target DL models or the others. In this paper, a novel attack method called Ensemble and Transfer Adversarial Attack (ETAA) is proposed to improve the transferability of adversarial attacks across different DL models. This method has a general framework and is able to work with various existing gradient-based attacks. Moreover, to reduce the power company's awareness of FDI attack in the demand data, a zero-mean plane projection is applied to limit the perturbations during adversarial data generation. From the evaluation results, it is proven that the proposed ETAA method can achieve higher attack success rate across different models and the zero-mean projection method can keep the final total adversarial power demand to be closer to the original normal demand.

Blockchain-Integrated Resilient Distributed Energy Resources Management System

Seerin Ahmad, BoHyun Ahn, Taesic Kim and Jinchun Choi (Texas A&M University-Kingsville, USA); Myungsuk Chae and Dongjun Han (Inha University, USA); Dong Jun Won (Inha University, Korea (South))

0
Distributed energy resource management system (DERMS) is a supervision system managing distributed energy resources (DERs) in a distribution system. However, the centralized DERMS has a potential risk of a single point of failure posed by cyber-attacks (e.g., denial of service attacks and ransomware attacks). This will cause visibility and control losses of the DER system. In this paper, blockchain (BC) technology is leveraged to enhance the resilience of DERMS by recovering the operation of a DER system during the DERMS outage. The proposed BC system is a governance platform for the DER system proving security and resilient control services on behalf of the DERMS until the availability of the DERMS is recovered. The feasibility of the proposed BC-integrated DERMS system toward a resilient DER system is validated by using a cyber-physical co-simulation testbed.

Session Chair and Room

Ertem Esiner (Advanced Digital Sciences Center, Singapore) — Room LT2

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Session CN1

Wireless Communications and Networking for Smart Grids

Conference
3:30 PM — 5:20 PM +08
Local
Oct 26 Wed, 3:30 AM — 5:20 AM EDT

A Wireless-Assisted Hierarchical Framework to Accommodate Mobile Energy Resources

Pudong Ge, Cesare Caputo, Fei Teng, Michel-Alexandre Cardin and Anna Korre (Imperial College London, United Kingdom (Great Britain))

0
The societal decarbonisation fosters the installation of massive renewable inverter-based resources (IBRs) in replacing fossil fuel based traditional energy supply. The efficient and reliable operation of distributed IBRs requires advanced Information and Communication Technologies (ICT), which may lead to a huge infrastructure investment and long construction time for remote communities. Therefore, to efficiently coordinate IBRs, we propose a low-cost hierarchical structure, especially for remote communities without existing strong ICT connections, that combines the advantages of centralised and distributed frameworks via advanced wireless communication technologies. More specifically, in each region covered by a single cellular network, dispatchable resources are controlled via a regional aggregated controller, and the corresponding regional information flow is enabled by a device-to-device (D2D) communication assisted wireless network. The wireless network can fully reuse the bandwidth to improve data flow efficiency, leading to a flexible information structure that can accommodate the plug-and-play operation of mobile IBRs. Simulation results demonstrate that the proposed wireless communication scheme significantly improves the utilization of existing bandwidth, and the dynamically allocated wireless system ensures the flexible operation of mobile IBRs.

A real-time cyber-physical testbed to assess protection system traffic over 5G networks

Charles Mawutor Adrah (Norwegian University of Science and Technology & NTNU, Norway); Mohammad Khalili Katoulaei (Norwegian University of Science and Technology, Norway & NTNU, Norway); Tesfaye Amare Zerihun (Sintef Energy AS, Norway); David Palma (NTNU, Norwegian University of Science and Technology & Faculty of Information Technology and Electrical Engineering (IE), Norway)

2
The fifth-generation (5G) mobile network promises to offer low latency services. Hence, there is interest in assessing various power distribution grid applications that are deployed with a 5G infrastructure. This paper presents a smart grid cyber-physical testbed for protection systems. It consists of power system models deployed on OPAL-RT, a real-time platform, and a 5G communication network modeled in ns-3. The testbed is used to assess the performance of a power system protection application (Permissive Underreaching Transfer Trip (PUTT) protection scheme) deployed over a 5G communication network. The proposed approach enables real-time protection traffic to be analyzed in an emulated 5G network and gives insights into how such a testbed can be used to assess the performance of protection traffic in 5G networks and beyond.

Smart Grid Critical Traffic Routing and Link Scheduling in 5G IAB Networks

Mohand Ouamer Nait Belaid (EDF R&D & University of Gustave Eiffel, France); Vincent Audebert (EDf R&D, France); Boris Deneuville (EDF R&D, France); Rami Langar (Ecole de Technologie Supérieure de Montréal and University Gustave Eiffel, Canada)

1
The increased integration of distributed energy resources (DERs) results in a two-way dynamic operation of the power distribution grid. Consequently, conventional Protection, Automation, and Control (PAC) systems are not able to manage DER related constraints in the distribution grid. New Fault location, Isolation, and service Recovery (FLISR) schemes based on communication capabilities are gaining a lot of momentum. Together with the 5th generation of mobile networks (5G), they improve the reactivity and the coordination of the grid defense lines. In this context, we present in this paper a FLISR traffic management framework in 5G Integrated Access and Backhaul (IAB) networks. Our framework consists first in optimizing the placement of FLISR protection functions within the Radio Access Network (RAN). Then, a joint routing and link scheduling of FLISR traffic in the 5G-RAN is proposed by taking into account the energy consumption. To achieve this, we formulate the master problem as two correlated integer linear programs (ILP) and present an optimal solution to solve it. Our objective is to find the best trade-off between the achieved network throughput and energy consumption, while ensuring the latency constraint of FLISR traffic. Our approach is compliant with the Software-Defined Radio Access Network (SD-RAN) paradigm since it can be integrated as a control flow application on top of a SD-RAN controller. Through a case study, we show that our proposed approach achieves significant gains in terms of energy consumption, flow acceptance and achieved network throughput, compared to baseline routing and placement strategies.

Resource Allocation for Intelligent Reflecting Surface-Assisted Cooperative NOMA-URLLC Networks in Smart Grid

Junjie Yang (Xidian University, China); Geng Liu (Smart Shine Microelectronics Technology Co., Ltd., China); Jie Ren (Beijing Smart-chip Microelectronics Technology Co., Ltd., China); Ying Liu and Liang Yao (Smart Shine Microelectronics Technology Co., Ltd., China); Yuchen Zhou and Jian Chen (Xidian University, China)

0
In this paper, we consider the resource allocation of mission-critical services in the smart grid, where we deploy an intelligent reflecting surface (IRS) during the transmission to alleviate the shortage of cooperative non-orthogonal multiple access (C-NOMA) in ultra-reliable and low-latency communications (URLLC). The purpose of this paper is to jointly optimize the power allocation, IRS phase shift, and the blocklength with finite blocklength information theory to minimize the total energy consumption subject to their delay and reliability constraints. Since the formulated optimization is non-convex, we first introduced two lemmas to simplify the constraints, and then we solve the optimization problem via the alternating optimization (AO), in which the transmit power and the blocklengths are optimized by using the techniques of successive convex approximation (SCA) and arithmetic geometry mean (AGM), and the reflective beamforming is optimized by using the techniques of semi-define relaxation (SDR) and sequential rank-one constraint relaxation (SROCR). Simulation results validate the advantage of IRS to C-NOMA in URLLC and the effectiveness of the resource allocation.

Design of a 5G Network Slicing Architecture for Mixed-Critical Services in Cellular Energy Systems

Dennis Overbeck, Fabian Kurtz, Stefan B枚cker and Christian Wietfeld (TU Dortmund University, Germany)

0
The shift towards renewable energies is increasing communication demands, particularly in novel grid architectures. One such approach is the concept of cellular energy systems, which divide the grid into regions with the potential to operate independently in case of emergencies. Management of the resulting energy flows between and within cells is highly complex. Thus communication becomes increasingly challenging. A promising method for handling the resulting mixed-critical data flows is the fifth generation of mobile radio networks, i.e. 5G. It enables reliable communication in public and private infrastructures via network slicing. Here, a single physical network is split up into multiple slices, each addressing the requirements of various services and devices optimally. This enables cost-efficient communications based on widely available Information and Communications Technology (ICT) infrastructures. In this work we provide an integrated architecture as well as a physical cellular
energy system testing setup. This is supported by an open-source 4G/5G software stack and gateways for handling mixed-critical grid communications. The physical testbed is located at the Smart Grid Technology Lab (SGTL) at TU Dortmund university and enables real-world analysis of relevant scenarios. Results illustrate the capabilities of network slicing in the Radio Access Network (RAN) and provide useful insights on deploying dedicated mobile radio networks in cellular energy systems with mixed-critical services.

Session Chair and Room

Utku Tefek (Advanced Digital Sciences Center, Singapore) — Room TT2-3

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Session CO1

Energy Management and Trading

Conference
3:30 PM — 5:20 PM +08
Local
Oct 26 Wed, 3:30 AM — 5:20 AM EDT

PEMT-CoSim: A Co-Simulation Platform for Packetized Energy Management and Trading in Distributed Energy Systems

Yuanliang Li (Concordia University, Canada); Luyang Hou (Beijing University of Posts and Telecommunications, China); Hang Du and Jun Yan (Concordia University, Canada); Yuhong Liu (Santa Clara University, USA); Mohsen Ghafouri (Concordia University, Canada); Peng Zhang (Shenzhen University, China)

0
The integration of Internet-of-Things, artificial intelligence, and other emerging technologies is driving the transition from traditional electricity consumers to energy prosumers in smart grids. To encourage flexible participation of prosumers in decentralized/distributed power management and trading, packetized energy (PE) technology is developed to manage prosumers' load and distributed energy resources in a request-reply way by encapsulating the energy into modulated, routable, and trackable electric packets of fixed duration and/or power for PE management (PEM) and trading (PET). To facilitate the research on PE and investigate PE-related problems in real-world environments, this paper proposes an open-source co-simulation platform called PEMT-CoSim based on the Transactive Energy Simulation Platform (TESP) for PEM and PET, which integrates PE into the management for prosumers in distribution systems by developing a dedicated Application Programming Interfaces (API) for PEM and PET. We also validate our simulation platform through PEM and PET case studies to showcase how to manage the power packages with prosumers' active participation in distributed energy systems.

An Optimization Framework for Effective Flexibility Management for Prosumers

Thanasis G. Papaioannou and George Stamoulis (Athens University of Economics and Business, Greece)

0
Energy flexibility management can significantly support the smoother and more cost-effective green transformation of the energy mix. However, effective management of the flexibility of residential loads can only be achieved if users are successfully engaged into the process. In this paper, we propose an optimization framework that incorporates provision of different forms of monetary and non-monetary incentives to prosumers, i.e., rewards, lotteries, peer-pressure, for providing flexibility at specific time slots. Economic rewards are offered according to a simple, yet very powerful, linear incentives' function. Dynamic tariffs per time slot for purchasing and selling electricity are accommodated in this framework as well. The overall problem of the DR aggregator is modeled as a cost-minimization one; its solution as a Stackelberg game is outlined for the case of full information on user utilities by the DR aggregator. Moreover, a distributed iterative algorithm is developed for solving the flexibility-management problem in the case where user-utility functions are not known to the aggregator. Numerical results show that this optimization framework is able to elicit the required flexibility from users at a minimum incentive cost, especially when monetary rewards are combined with peer pressure.

Optimal Dynamic Multi-source Multi-community Power Schedule and Trading

Olamide Jogunola and Bamidele Adebisi (Manchester Metropolitan University, United Kingdom (Great Britain)); Haris Gačanin (RWTH Aachen University, Germany); Mohammad Hammoudeh (College of Computing and Mathematics, King Fahd University of Petroleum & Minerals, Dhahran, 31261, KSA, Saudi Arabia.); Guan Gui (Nanjing University of Posts and Telecommunications, China)

0
As peer-to-peer (P2P) energy trading and local energy market (LEM) are gaining momentum, a follow-up challenge is scaling up to include multi-community, multi-region power schedule and trading. This study introduces community-to-community power trading and schedules considering various generating units, including coal, gas, wind, and solar, as well as import and export prices from community transactions. These generating sources are used to fulfil the demand requirements of each community over a time horizon, as well as absorbing or trading the margin among the inter-connected communities, while fulfilling certain distribution network constraints. For a practical case, the uncertainties in wind power generations are considered. An optimality condition decomposition technique is used to decompose the overall problem into a community-based local problem. Thus, individual community solves their optimisation local problem in parallel for operational efficiency of real-time multi-commodity power schedule and trading. The initial results indicate that each community acts in its best interest to minimise its costs when there is a change in the variable. When emission costs are applied as a constraint to their generation, a reduction in power generation is observed augmented by an increase of up to 30.8% of power transferred in the inter-community transaction.

Projection-aware Deep Neural Network for DC Optimal Power Flow Without Constraint Violations

Minsoo Kim and Hongseok Kim (Sogang University, Korea (South))

2
Solving optimal power flow is essential to ensure stable and cost effective power system operations. However, frequently solving optimal power flow under numerous scenarios using conventional solvers suffers from high computational complexity due to the large-scale power network. As a substitute, using deep neural network to solve optimal power flow draws researchers' attention. Even though deep neural network is beneficial for reducing the computational time of solving optimal power flow, its outputs often violate physical constraints of the power network. To overcome the constraint violations of deep neural network, we propose projection-aware deep neural network (PA-DNN) for solving optimal power flow. To the best of our knowledge, this is the first paper that guarantees no constraint violations of DC optimal power flow using deep neural network. The proposed PA-DNN takes active power demand as an input and has a projection layer at the final layer. Through the projection layer, hidden vectors are projected onto a feasible region. Then, by minimizing error between the projected vector and optimal active power generation, PA-DNN learns to predict accurate generation dispatch. Simulation results on various PGLib-OPF networks show that PA-DNN achieves nearly zero optimality gap with no constraint violations using only 15% of the training data of the baseline.

Cooperative Carbon Emission Trading: A Coalition Game Approach

Qisheng Huang (Harbin Institute of Technology Shenzhen, China); Yunshu Liu (The Chinese University of Hong Kong & Shenzhen Institute of Artificial Intelligence and Robotics for Society, Hong Kong); Peng Sun (The Chinese University of Hong Kong, Shenzhen, China); Junling Li (Southeast University, China); Jin Xu (The Chinese University of Hong Kong, Shenzhen, China & University of Science and Technology of China, China)

0
Many countries have implemented different policies to achieve carbon neutrality in the current century. The cap-and-trade policy is one of the popular policies. The cap-and-trade policy provides carbon emission quotas for power generation companies. Each company must carefully determine its energy production based on the carbon emission quota and demand uncertainty. In this paper, we analyze the cooperation among different power generation companies using the coalition game theory. We show the optimality of the grand coalition for minimizing the total cost by proving that the cost function is subadditive. This result highlights the benefits of cooperation. We further propose a cost allocation mechanism that allocates the total cost to different power generation companies. We prove that the proposed cost allocation mechanism is in the core of the coalition game such that no group of power generation companies has any incentive to leave the grand coalition. Numerical experiments have been conducted to validate the established theoretical results.

Session Chair and Room

Jip Kim (KENTECH, Korea / Columbia University, USA) — Room LT2

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Session P1

Panel: Beyond 5G/6G Communication Technologies for Sustainable Industries

Conference
5:30 PM — 7:00 PM +08
Local
Oct 26 Wed, 5:30 AM — 7:00 AM EDT

Panel: Beyond 5G/6G Communication Technologies for Sustainable Industries

Moderator: Prof Haris Gačanin, RWTH Aachen University; Panelist: Aydin Sezgin, Sumei Sun, Iwao Hosaka, Dejan Vukobratovic

0
Previous generations of mobile communications technologies mainly targeted exchanging information via voice, data/video, and the Internet communications. Following the efforts of the BMBF 6GEM Research Hub in Germany, we address the research efforts of technology evolution toward 6G networks for industrial and experience-rich (i.e., extended reality, etc.) communications to digitalize the industrial applications. In particular, the vertical sectors such as automotive, production, healthcare, and logistics are early adopters of industry digitalization in the future. Therefore, this panel aims at shedding light on the ample opportunities and challenges with this fundamental change in communication technologies.

Session Chair and Room

Binbin Chen (Singapore University of Technology and Design, Singapore) — Room LT2

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