IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (IEEE SmartGridComm 2020)
Angela Yingjun Zhang (CUHK) & György Dán (KTH)
Load and Price Forecasting
Real-time Locational Marginal Price Forecasting Using Generative Adversarial Network
Zhongxia Zhang and Meng Wu (Arizona State University, USA)
Electricity Load Forecasting with Collective Echo State Networks
Siwu Liu and Chenxiao Xu (Stony Brook University, USA); Ying Liu and Dimitrios Katramatos (Brookhaven National Laboratory, USA); Shinjae Yoo (Brookhaven National Lab, USA)
Coordinated Demand Response By Data Centers Using Inverse Optimization
Athanasios Tsiligkaridis, Ioannis Paschalidis and Ayse Coskun (Boston University, USA)
Load Approximation for Uncertain Topologies in the Low-Voltage Grid
Ludovic Mouline, Maxime Cordy and Yves Le Traon (University of Luxembourg, Luxembourg)
Chen Chen (Xi'an Jiaotong University)
Combflex: a Linear Combinatorial Auction for Local Energy Markets
Diego Kiedanski (Telecom ParisTech, France); Ariel Orda (Technion, Israel); Daniel Kofman (Telecom Paristech, France)
Unfortunately, traditionally proposed implementations of local energy markets such as simple double auctions and peer to peer exchanges do not fully exploit the available flexibility in these systems.
We design a market mechanism that exploits the characteristics of the players, providing them with expressive bids to represent their flexibility, which we assume is due to energy storage.
The proposed market is not obviously manipulable and can be cleared by solving a linear programming problem that grows linearly in the number of participants.
Using realistic data, we benchmark the proposed mechanism against sequential auctions and peer to peer exchanges often used in the literature.
Our numerical results show that the proposed mechanism outperforms traditional implementations.
Misalignments of Objectives in Demand Response Programs: A Look at Local Energy Markets
Diego Kiedanski (Telecom ParisTech, France); Daniel Kofman (Telecom Paristech, France); Patrick Maillé (IMT Atlantique, France); Jose Horta (Telecom ParisTech, France)
During the progressive release of LEMs, the decision problem faced by prosumers (consumers that might also produce energy), will differ from the wholesale electricity market's one because there is always the alternative to buy from or sell to the utility company.
In this setting, guaranteeing that the aggregated energy consumption will be well behaved depends on the properties of the mechanisms used to implement the market, the alternative tariff offered to participants by their utility and how prosumers interact among themselves.
We present a pathological example of a LEM in which the best strategy for the agents results in unnecessary peaks of demand.
A decision model for players participating in LEMs is developed to study the existence of undesirable behaviour while using realistic data and number of participants.
Through numerical experiments, we identify the key aspects of the player's behaviour, strategy and environment that lead to the aforementioned peaks, all under reasonable circumstances.
Simple fixes are discussed to overcome the pitfalls of such markets.
DER Information Unaware Coordination via Day-ahead Dynamic Power Bounds
Thomas Navidi, Chloe Leblanc, Abbas El Gamal and Ram Rajagopal (Stanford University, USA)
An Incentive Compatible Market Mechanism for Integrating Demand Response into Power Systems
Ce Xu, Hossein Khazaei and Yue Zhao (Stony Brook University, USA)
Yue Zhao (Stony Brook Univ.)
Privacy, Attacks and Mitigation
Towards Privacy-Preserving Anomaly-based Attack Detection against Data Falsification in Smart Grid
Yu Ishimaki (Waseda University, Japan); Shameek Bhattacharjee (Western Michigan University, USA); Hayato Yamana (Waseda University, Japan); Sajal K. Das (Missouri University of Science and Technology, USA)
Optimal Cyber Defense Strategy of High-Voltage DC Systems for Frequency Deviation Mitigation
Jiazuo Hou (The University of Hong Kong, China); Shunbo Lei (University of Michigan, USA); Wenqian Yin (The University of Hong Kong, China); Chaoyi Peng (China Southern Power Grid, China); Yunhe Hou (University of Hong Kong, China)
An Attack-Trace Generating Toolchain for Cybersecurity Study of IEC 61850 based Substations
Partha P. Biswas, Yuan Li, Heng Chuan Tan and Daisuke Mashima (Advanced Digital Sciences Center, Singapore); Binbin Chen (Singapore University of Technology and Design, Singapore)
allows them to better support various advanced operations.
However, it also poses greater risk of cyber-related attacks. There
is an array of cyber-security solutions (e.g., intrusion detection systems) available in the market to prevent, detect, or respond to cyberattacks. This calls for the creation of datasets and test cases for the validation of those cyber-security solutions. In our recent work, we have generated a synthesized dataset for testing of cyber-security solutions of IEC 61850 based substations. Our
dataset contained traces for some typical attack-free disturbance
scenarios and cyberattack scenarios in a substation. In this
work, we present the toolchain we have developed to allow easy
generation of such traces. We discuss the design considerations
behind our toolchain and provide step-by-step guide to potential
users on how to create customized trace files for specific scenarios
using our toolchain. By open-sourcing the project for the broad
community, we hope our toolchain will enrich the body of testing
datasets for substation cyber security solutions.
A Cyber-Resilient Privacy Framework for the Smart Grid with Dynamic Billing Capabilities
Gaurav S. Wagh and Sumita Mishra (Rochester Institute of Technology, USA)
Data Analytics for Grid II
Household Level Electricity Load Forecasting using Echo State Network
Debneil Saha Roy (Stony Brook University, USA)
Transfer Learning for Operational Planning of Batteries in Commercial Buildings
Brida Mbuwir (KU Leuven & EnergyVille, Belgium); Kaveh Paridari (KTH Royal Institute of Technology, Sweden); Fred Spiessens (VITO, Belgium); Lars M Nordström (Royal Institute of Technology, KTH, Sweden); Geert Deconinck (KU Leuven, Belgium)
Exploiting Satellite Data for Solar Performance Modeling
Akansha Singh Bansal (University of Massachusetts Amherst, USA); David Irwin (University of Massachusetts, Amherst, USA)
In this paper, we develop and evaluate solar performance models that use satellite-based estimates of downward shortwave (solar) radiation (DSR) at the Earth's surface, which NOAA began publicly releasing after the launch of the GOES-R geostationary satellites in 2017. Unlike public weather data, DSR estimates are available for every 0.5km 2 area. As we show, the accuracy of solar performance modeling using satellite data and public weather station data depends on the cloud conditions, with DSR-based modeling being more accurate under clear skies and station-based modeling being more accurate under overcast skies. Surprisingly, our results show that, overall, pure satellite-based modeling yields similar accuracy as pure station-based modeling, although the relationship is a function of conditions and the local climate. We also show that a hybrid approach that combines the best of both approaches can also modestly improve accuracy.
Predictive Maintenance for Increasing EV Charging Load in Distribution Power System
Salman Shuvo and Yasin Yilmaz (University of South Florida, USA)
introduces a high intensity charging load to the power system.
The distribution systems are not well prepared to cope with this
high variance load. To handle such EV charging load, utility
companies need a predictive maintenance approach for the distribution transformers. We propose a deep reinforcement learning
(RL) based policy to timely replace the distribution transformers
by similar or higher capacity ones under a budgetary constraint
of selecting at most one transformer for replacement per time
step. Our policy outperforms the myopic policies which replace
transformers based on load, age, and failure in terms of both
economic cost and power outage.
SoDa: An Irradiance-Based Synthetic Solar Data Generation Tool
Ignacio Losada Carreno (ASU, USA); Raksha Ramakrishna and Anna Scaglione (Arizona State University, USA); Daniel Arnold (Lawrence Berkeley National Laboratory, USA); Ciaran Roberts (Lawrence Berkeley National Lab & UC Berkeley, USA); Sy-Toan Ngo and Sean Peisert (Lawrence Berkeley National Laboratory, USA); David Pinney (National Rural Electric Cooperative Association, USA)
Chen Chen (Xi'an Jiaotong University)
Storage and attacks
Time-of-Use and Demand Charge Battery Controller using Stochastic Model Predictive Control
Michael Blonsky (National Renewable Energy Laboratory & Colorado School of Mines, USA); Killian McKenna (National Renewable Energy Laboratory, USA); Tyrone Vincent (Colorado School of Mines, USA); Adarsh Nagarajan (National Renewable Energy Laboratory, USA)
Impact Analysis of EV Preconditioning on the Residential Distribution Network
Joseph Antoun, Mohammad Ekramul Kabir, Ribal Atallah, Bassam Moussa, Mohsen Ghafouri and Chadi Assi (Concordia University, Canada)
We find out that reconfiguration will be able to aid the performance of the network to an average EV penetration rate.
Grid-Coupled Dynamic Response of Battery-Driven Voltage Source Converters
Ciaran Roberts (Lawrence Berkeley National Lab & UC Berkeley, USA); Jose Daniel Lara and Rodrigo Henriquez-Auba (University of California, Berkeley, USA); Bala K Poolla (National Renewable Energy Laboratory, USA); Duncan Callaway (UC Berkeley, USA)
Model-Agnostic Algorithm for Real-Time Attack Identification in Power Grid using Koopman Modes
Sai Pushpak Nandanoori, Soumya Kundu, Seemita Pal, Khushbu Agarwal and Sutanay Choudhury (Pacific Northwest National Laboratory, USA)
Duncan Callaway (UC Berkeley)
Distribution Systems and DER
Voltage Regulation and Protection for Power Distribution Systems using Reinforcement Learning
Dileep Kalathil (TAMU)
Optimal Coordination of High and Low Voltage Systems to Leverage DERs
Lindsay Anderson (Cornell)
Grid Optimization under Emerging Constraints
Balancing Wildfire Risk and Power Outages through Optimized Power Shut-offs
Line Roald (UW-Madison)
Electricity and Water Do Mix: Interdependent Electric and Water Infrastructure Modeling, Optimization and Control
Vijay Vittal (ASU)
System Security and Modeling
Benefits and Cyber-Vulnerability of Demand Response System in Real-Time Grid Operations
Mingjian Tuo, Arun Venkatesh Ramesh and Xingpeng Li (University of Houston, USA)
Making Renewable Energy Certificates Efficient, Trustworthy, and Anonymous
Dimcho Karakashev and Sergey Gorbunov (University of Waterloo, Canada); Srinivasan Keshav (University of Cambridge, United Kingdom (Great Britain))
Online Reasoning about the Root Causes of Software Rollout Failures in the Smart Grid
Ewa Piatkowska, Catalin Gavriluta, Paul Smith and Filip Pröstl Andren (AIT Austrian Institute of Technology, Austria)
Stacked Metamodels for Sensitivity Analysis and Uncertainty Quantification of AMI Models
Michael Rausch (University of Illinois at Urbana-Champaign, USA); William Sanders (Carnegie Mellon University, USA)
Dynamic State Estimation Based Monitoring of High Frequency Transformer
Boqi Xie (Georgia Institute of Technology, USA); Dongbo Zhao (Argonne National Laboratory, USA); Tianqi Hong (NYU Polytechnic School of Engineering, USA); Alex Huang (University of Texas at Austin, USA); Zhicheng Guo (UT Austin, USA); Yuzhang Lin (University of Massachusetts, Lowell, USA)
Learning to Optimize Power Distribution Grids using Sensitivity-Informed Deep Neural Networks
Manish Kumar Singh, Sarthak Gupta and Vassilis Kekatos (Virginia Tech, USA); Guido Cavraro and Andrey Bernstein (National Renewable Energy Laboratory, USA)
Restoring Distribution System Under Renewable Uncertainty Using Reinforcement Learning
Xiangyu Zhang, Abinet Tesfaye Eseye, Bernard Knueven and Wesley Jones (National Renewable Energy Laboratory, USA)
Deep Reinforcement Learning for DER Cyber-Attack Mitigation
Ciaran Roberts (Lawrence Berkeley National Lab & UC Berkeley, USA); Sy-Toan Ngo (Lawrence Berkeley National Laboratory, USA); Alexandre Milesi (Lawrence Berkeley National Lab, USA); Sean Peisert and Daniel Arnold (Lawrence Berkeley National Laboratory, USA); Shammya S Saha, Anna Scaglione and Nathan G Johnson (Arizona State University, USA); Anton Kocheturov and Dmitriy Fradkin (Siemens Corporation Corporate Technology, USA)
Edge Layer Design and Optimization for Smart Grids
Adetola Adeniran and Md Abul Hasnat (University of South Florida, USA); Minoo Hosseinzadeh and Hana Khamfroush (University of Kentucky, USA); Mahshid Rahnamay-Naeini (University of South Florida, USA)
Distributed Anomaly Detection and PMU Data Recovery in a Fog-computing-WAMS Paradigm
Kaustav Chatterjee (The Pennsylvania State University, USA); Nilanjan Chaudhuri (Pennsylvania State University, USA)
Optimal Smart Grid Operation and Control Enhancement by Edge Computing
Yuan Liao and Jiangbiao He (University of Kentucky, USA)
Accelerating AI on the Grid: A Hands On Tutorial on PMU Data Analysis
Alexandra von Meier (UC Berkeley), Kevin Jones (Dominion Energy), Laurel Dunn (UC Berkeley), Mohini Bariya (UC Berkeley), Miles Rusch (UC Berkeley), Sean Murphy (PingThings)
Lalitha Sankar (ASU)