Workshop on Power System Optimization and Machine Learning Models (PSO-ML)


Technological developments and environmental concerns have led to an unprecedented evolution of energy systems. In the new paradigm, advanced energy resource management is needed to enable the intensive integration of distributed energy resources, taking advantage of its full potential. Furthermore, modern power systems require a satisfactory level of flexibility over different time horizons to maintain operation at a reasonable cost and satisfy restrictions imposed by the distribution system operator. Despite the improvement in forecasting techniques, it is a fact that power systems were not designed to effectively deal with the increased levels of uncertainty associated with distributed energy resources.

Due to memory requirements and limited runtimes, current works are often restricted to small cases in terms of problem size and are rarely able to consider all sources of uncertainty together, thus lacking realism. Computational intelligence approaches are proving to be very effective in dealing with large problems with nonlinearities, and new metaheuristics have been continually proposed in the literature to mitigate these problems. Moreover, recent advances in the field of machine learning are enabling reaching more accurate predictions of generation and demand, consequently reducing the uncertainty, and contributing to a more efficient power system management and operation.

PSO-ML aims at providing an advanced discussion forum on recent and innovative work in the fields of machine learning and optimization as solutions to address power system related problems. Special relevance is indorsed to solutions involving the application of artificial intelligence approaches, including metaheuristic optimization, automated machine learning, , forecasting methodologies and agent-based systems.


Relevant submission topics include (not limited to):

  • Agent-based Approaches for Microgrid Management
  • Agent-based Building Management Systems
  • Agent-based methods for Demand Management
  • Big Data Applications for Energy Systems
  • Coalitions and Aggregations of Smart Grid and Market Players
  • Consumer Profiling
  • Context Aware Systems
  • Data-Mining Approaches
  • Decision Support Models
  • Demand Response Aggregation
  • Demand Response Integration in the Market
  • Demand Response Remuneration Methods
  • Electricity Market Modelling and Simulation
  • Electricity Market Negotiation Strategies
  • Energy Resource Management
  • Innovative Demand Response Models and Programs
  • Innovative Energy Tariffs
  • Integration of Electric Vehicles in the Power System
  • Intelligent Supervisory Control Systems
  • Intelligent Resources Scheduling in Smart Grids
  • Load Forecast
  • Market Models for Variable Renewable Energy
  • Multi-Agent Applications for Smart Grids
  • Other Artificial Intelligence-based Methods for Power Systems
  • Phasor Measurement Units Applications
  • Reliability, Protection and Network Security Methods
  • Renewable Energy Forecast
  • Smart Grid Simulation
  • Smart Sensors and Advanced Metering Infrastructure


Artificial Intelligence; Energy Resources Management; Electricity Markets; Machine Learning; Metaheuristic Optimization; Multi-Agent Systems; Renewable Energy;

Organizing Committee

  • João Soares, Polytechnic of Porto (Portugal)
  • Leonardo H. Macedo, Universidade Estadual Paulista (Brazil)
  • Tiago Pinto, Universidade de Trás-os-Montes e Alto Douro (Portugal)


João Soares
Polytechnic of Porto (Portugal)

Leonardo H. Macedo
Universidade Estadual Paulista (Brazil)

Tiago Pinto
Universidade de Trás-os-Montes e Alto Douro (Portugal)

General deadlines

  • Deadline

    15th March, 2024
    5th April, 2024
    12th April, 2024

  • Doctoral Consortium deadline

    22nd March, 2024
    12th April, 2024
    26th April, 2024

  • Notification of acceptance

    26th April, 2024
    3rd May, 2024

  • Camera-Ready papers

    17th May, 2024

  • Conference Celebration

    26th-28th June, 2024


All proposed papers must be submitted in electronic form (PDF format) using the ISAmI conference management system.