IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (IEEE SmartGridComm 2021) — recording passcode: SGC-VC21
Smart grid communications
Data Communication Interfaces in Smart Grid Real-time Simulations: Challenges and Solutions
Mehrdad Sheikholeslami; Zuyi Li
A novel load distribution strategy for aggregators using IoT-enabled mobile devices
Nitin Shivaraman; Jakob Fittler; Saravanan Ramanathan; Arvind Easwaran; Sebastian Steinhorst
Effect of 5G communication service failure on placement of Intelligent Electronic Devices in Smart Distribution Grids
Romina Muka; Michele Garau; Besmir Tola; Poul E. Heegaard
Graph Convolution Networks-Based Island Partition for Energy and Information Coupled Active Distribution Systems
Qiyue Li; Shengquan Dai; Ximing Li; Weitao Li; Wei Sun
Modelling framework for study of distributed and centralized smart grid system services
Fredrik B Haugli; Poul E. Heegaard
Distribution grids and privacy
Poisoning Attack against Event Classification in Distribution Synchrophasor Measurements
Mohasinina Kamal; Alireza Shahsavari; Hamed Mohsenian-Rad
Learning Sparse Privacy-Preserving Representations for Smart Meters Data
Mohammadhadi Shateri; Francisco Messina; Pablo Piantanida; Fabrice Labeau
Electricity Theft Detection in the Presence of Prosumers Using a Cluster-based Multi-feature Detection Model
Arwa Alromih; John Clark; Prosanta Gope
Exploiting DLMS/COSEM Data Compression To Learn Power Consumption Patterns
Marcell Fehér; Daniel E. Lucani; Morten Tranberg Hansen; Flemming Enevold Vester
Adversarial Machine Learning Against False Data Injection Attack Detection for Smart Grid Demand Response
Guihai Zhang; Biplab Sikdar
Workshop on Charging Solutions for Electro-mobility of the Future
Optimisation and AI in smart grids
Low-complexity Risk-averse MPC for EMS
Johannes Philippus Maree; Sebastien Gros; Venkatachalam Lakshmanan
A World Model Based Reinforcement Learning Architecture for Autonomous Power System Control
Magnus Tarle; Mårten Björkman; Mats Larsson; Lars Nordström; Gunnar Ingeström
In this paper we demonstrate how a model based reinforcement learning (MBRL) algorithm, which learns and uses an internal model of the world, can be used for autonomous power system control. The proposed RL agent, called the World Model for Autonomous Power System Control (WMAP), includes a safety shield to minimize risk of poor decisions at high uncertainty. The shield can be configured to permit WMAP to take actions with the condition that WMAP asks for guidance, e.g. from a human operator, when in doubt. As an alternative, WMAP could be run in full decision support mode which would require the operator to take all the active decisions.
A case study is performed on a IEEE 14-bus system where WMAP is setup to control setpoints of two FACTS devices to emulate grid stability improvements. Results show that improved grid stability is achieved using WMAP while staying within voltage limits. Furthermore, a disastrous situation is avoided when WMAP asks for help in a test scenario event that it had not been trained for.
Towards Strategic Local Power Network Decarbonization: A Stackelberg Game Analysis
Qisheng Huang; Jianwei Huang
Performance Evaluation of an Advanced Distributed Energy Resource Management Algorithm
Jing Wang; Jeff Simpson; Rui Yang; Bryan Palmintier; Soumya Tiwari; Yingchen Zhang
Benchmarking a Decentralized Reinforcement Learning Control Strategy for an Energy Community
Niklas Ebell; Marco Pruckner
Power Systems Application Security
Modeling of Cyber Attacks Against Converter-Driven Stability of PMSG-Based Wind Farms with Intentional Subsynchronous Resonance
Hang Du; Jun Yan; Mohsen Ghafouri; Rawad Zgheib; Marthe Kassouf; Mourad Debbabi
Vulnerabilities of Power System Operations to Load Forecasting Data Injection Attacks
Yize Chen; Yushi Tan; Ling Zhang; Baosen Zhang
Securing SCADA networks for smart grids via a distributed evaluation of local sensor data
Verena Menzel; Johann Hurink; Anne Remke
Distort to Detect, not Affect: Detecting Stealthy Sensor Attacks with Micro-distortion
Suman Sourav; Binbin Chen
Enabling the Data-driven Future
Data-drive computational methods for grid operation
Physics-Informed Neural Networks for Minimising Worst-Case Violations in DC Optimal Power Flow
Rahul Nellikkath; Spyros Chatzivasileiadis
Energy Blockchain for Demand Response and Distributed Energy Resource Management
Mikhak Samadi; Henry Schriemer; Sushmita Ruj; Melike Erol-Kantarci
Distributed Weighted Least-Squares and Gaussian Belief Propagation: An Integrated Approach
Dino Zivojevic; Muhamed Delalic; Darijo Raca; Dejan Vukobratović; Mirsad Cosovic
Learning without Data: Physics-Informed Neural Networks for Fast Time-Domain Simulation
Jochen Stiasny; Samuel Chevalier; Spyros Chatzivasileiadis
Matrix Completion for Improved Observability in Low-Voltage Distribution Grids
Marija Markovic; Anthony Florita; Bri-Mathias Hodge
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