IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids
31 October – 3 November 2023 // Glasgow, Scotland

Workshop on Learning and Optimization for Power Distribution System Resilience

IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) – Workshop on Learning and Optimization for Power Distribution System Resilience



The operation of power distribution systems is challenged by uncertainties and variabilities resulting from increasing renewable generation, demand-side activities, natural disasters, and cyber-physical attacks. As traditional rule-based approaches become ineffective, artificial and computational intelligence for situational awareness and decision-making are required for ensuring system operational performances. The continuing proliferation of smart sensing and communication devices, the significant growth of computational resources in centralized and distributed forms, and the rapidly advancing optimization and control theories and algorithms are forming a solid foundation for the conceptualization, development, and implementation of artificial and computational intelligence fulfilling the needs of future power distribution systems. One key area of applications, which draws significant attention from the technical community, is the enhancement of the resilience of power distribution systems. Resilience refers to a system’s capability of reacting to high-impact-low-probability events, such as natural disasters and cyber-physical attacks. Under disastrous conditions, the main objectives of system operation are to predict power outages, detect and identify threats, assess asset damages, isolate faulty components and areas, minimize service interruptions, and restore power supply. It is also important to ensure energy efficiency, host renewable energy generation, minimize energy losses, and maintain power quality delivered to customers during this process. To successfully achieve these objectives, the artificial and computational intelligence must have high efficiency and scalability, robustness against data corruptions and attacks, and the capability of accommodating the heterogeneity and imperfection of the underlying cyber network that supports its functionality.

This workshop aims to bring together researchers and practitioners in the field of machine learning, optimization, statistics, and signal processing for presentation and discussion of artificial and computational intelligence for resilient power distribution systems.


Examples of methods of artificial and computational intelligence include, but are not limited to:

  • Spatio-temporal modeling
  • Graph learning and signal processing
  • Reinforcement learning
  • Federated learning
  • Generative modeling
  • Physics-informed machine learning
  • Machine learning with cybersecurity
  • Robust and stochastic optimization
  • Intelligent optimization methods
  • Detection and estimation algorithms
  • Multi-sensor data fusion
  • Complex network theory
  • Multi-agent decision making • Resilient control


Example applications of power distribution system resilience include, but are not limited to:

  • Outage prediction
  • State estimation and topology identification
  • Islanding detection
  • Fault localization
  • Cyber-attack detection and mitigation
  • Renewable hosting capacity estimation
  • Ultra-short-term forecasting
  • Asset damage assessment
  • System reconfiguration
  • Service restoration and crew dispatch
  • Control of energy storage systems
  • Dynamic modeling and stability
  • Microgrid formation and operation
  • Demand-side management
  • Cyber-physical interdependence modeling
  • Multi-energy system restoration


Important Dates

  • Paper submission deadline (regular and invited): Jul. 15th, 2023
  • Review results announced: Aug. 15th, 2023
  • Camera-ready regular and invited papers due: Sep. 1st, 2023


Workshop Chairs

  • Yue Zhao, Stony Brook University, USA.
  • Yuzhang Lin, New York University, USA.


TPC Members

  • Kyri Baker, University of Colorado at Boulder
  • Xin Chen, Massachusetts Institute of Technology
  • Anamika Dubey, Washington State University
  • Yury Dvorkin, Johns Hopkins University
  • Tong Huang, San Diego State University
  • Zhengmao Li, Aalto University
  • Manish K. Singh, University of Minnesota
  • Venkatesh Venkataramanan, National Renewable Energy Laboratory
  • Zhaoyu Wang, Iowa State University
  • Junbo Zhao, University of Connecticut