Smart Grid concepts are rapidly being transferred to the market and huge investments have already been made in renewable based electricity generation and in rolling out smart meters. However, the present state of the art does not ensure neither a good return of investment nor a sustainable and efficient power system. The work so far involves mainly larger stakeholders, namely power utilities and manufacturers and their main focus has been on the production and grid resources. This vision is missing a closer attention to the demand side and especially to the interaction between the demand side and the new methods for smart grid management.

Efficient power systems require, at all moments, the optimal use of the available resources to cope with demand requirements. Demand response programs framed by adequate business models will play a key-role in more efficient systems by increasing demand flexibility both on centralized and distributed models, particularly for the latter as renewable energy generation and storage is highly dependable of uncontrolled factors (such as wind and solar radiation) for which anticipated forecasts are subjected to significant errors.

The complexity and dynamic nature of these problems requires the application of advanced solutions to enable the achievement of relevant advancements in the state of the art. Artificial intelligence and distributed computing systems are, consequently, being increasingly embraced as a valuable solution. ADRESS aims at providing an advanced discussion forum on recent and innovative work in the fields of demand response and renewable energy sources integration in the power system. Special relevance is indorsed to solutions involving the application of artificial intelligence approaches, including agent-based systems, data-mining, machine learning methodologies, forecasting and optimization, especially in the scope of smart grids and electricity markets.


Artificial Intelligence; Demand Response; Energy Resources Management; Electricity Markets; Multi-Agent Systems; Renewable Energy; Smart Grids


  • Agent-based Approaches for Microgrid Management
  • Agent-based Home 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 in Smart Grids
  • Decision Support Approaches for Smart Grids
  • 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 in Buildings
  • 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


Organizing Committee

  • Zita Vale, Polytechnic of Porto (Portugal)
  • Pedro Faria, Polytechnic of Porto (Portugal)
  • Juan M Corchado, University of Salamanca (Spain)
  • Tiago Pinto, University of Salamanca (Spain)
  • Pierluigi Siano, University of Salerno (Italy)

Program Committee

  • Bo Norregaard Jorgensen, University of Southern Denmark (Denmark)
  • Carlos Ramos, Polytechnic of Porto (Portugal)
  • Cătălin Buiu, Politehnica University Bucharest (Romania)
  • Cédric Clastres, Institut National Polytechnique de Grenoble (France)
  • Dante I. Tapia, Nebusens (Spain)
  • Frédéric Wurtz, Institut National Polytechnique de Grenoble (France)
  • Georg Lettner, Vienna University of Technology (Austria)
  • Germano Lambert-Torres, Dinkart Systems (Brazil)
  • Gustavo Figueroa, Instituto de Investigaciones Eléctricas (Mexico)
  • Ines Hauer, Otto-von-Guericke-University Magdeburg (Germany)
  • Isabel Praça, Polytechnic of Porto (Portugal)
  • István Erlich, University of Duisburg-Essen (Germany)
  • Jan Segerstam, Empower IM Oy (Finland)
  • José Rueda, Delft University of Technology (The Netherlands)
  • Juan Corchado, University of Salamanca (Spain)
  • Juan F. De Paz, University of Salamanca (Spain)
  • Kumar Venayagamoorthy, Clemson University (US)
  • Lamya Belhaj, l’Institut Catholique d’Arts et Métiers (France)
  • Nikolaus Starzacher, Discovergy (Germany)
  • Nikos Hatziargyriou, National Technical University of Athens (Greece)
  • Marko Delimar, University of Zagreb (Croatia)
  • Nouredine Hadj-Said, Institut National Polytechnique de Grenoble (France)
  • Pablo Ibarguengoytia, Instituto de Investigaciones Eléctricas (Mexico)
  • Paolo Bertoldi, European Commission, Institute for Energy and Transport (Belgium)
  • Pierluigi Siano, University of Salerno (Italy)
  • Pedro Faria, Polytechnic of Porto (Portugal)
  • Peter Kadar, Budapest University of Technology and Economics (Hungary)
  • Pierre Pinson, Technical University of Denmark (Denmark)
  • Rodrigo Ferreira, Intelligent Sensing Anywhere (Portugal)
  • Stephen McArthur, University of Strathclyde, Scotland (United Kingdom)
  • Tiago Pinto, Polytechnic of Porto (Portugal)
  • Xavier Guillaud, École Centrale de Lille (France)
  • Zbigniew Antoni Styczynski, Otto-von-Guericke-University Magdeburg (Germany)
  • Zita Vale, Polytechnic of Porto (Portugal)


Zita Vale
Polytechnic of Porto (Portugal)

Pedro Faria
Polytechnic of Porto (Portugal)

Juan M Corchado
University of Salamanca (Spain)

Tiago Pinto
University of Salamanca (Spain)