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. Multi-agent based 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

  • Kumar Venayagamoorthy, Clemson University (US)
  • Zita Vale, Polytechnic of Porto (Portugal)
  • Juan M Corchado, University of Salamanca (Spain)
  • Tiago Pinto, University of Salamanca (Spain)

Program Committee

  • Alexandre Silva, General Electric Global Research, Brazil
  • Amin Shokri Gazafroudi, University of Salamanca, Spain
  • Bo Noerregaard Joergensen, University of Southern Denmark, Denmark
  • Carlos Ramos, Polytechnic of Porto, Portugal
  • Dagmar Niebur, Drexel University, USA
  • Dante I. Tapia, University of Salamanca, Spain
  • Elvira Amicarelli, CEA, France
  • Fernando Lezama, Instituto Nacional de Astrofísica, Óptica y Electronica, Mexico
  • Germano Lambert-Torres, PS Solutions, Brazil
  • Goreti Marreiros, Polytechnic of Porto, Portugal
  • Gustavo Arroyo, Electrical Research Institute, Mexico
  • Isabel Praça, Polytechnic of Porto, Portugal
  • Joao Soares, Polytechnic of Porto, Portugal
  • Jose L. Rueda, Delft University of Technology, The Netherlands
  • Kevin Tomsovic, University of Tennessee, USA
  • Kwang Y. Lee, Baylor University, USA
  • Marko Delimar, University of Zagreb, Croatia
  • Nouredine Hadj-Said, Institut National Polytechnique de Grenoble, France
  • Olivier Boissier, École Nationale Supérieure des Mines de Saint-Étienne, France
  • Pablo Chamoso, University of Salamanca, Spain
  • Pablo H. Ibarguengoytia, Instituto de Investigaciones Electricas, Mexico
  • Pedro Faria, Polytechnic of Porto, Portugal
  • Péter Kádár, Budapest University of Technology and Economics, Hungary
  • Rui Castro, Instituto Superior Técnico, Portugal
  • Tiago Sousa, Polytechnic of Porto, Portugal
  • Frédéric Wurtz, Institut National Polytechnique de Grenoble, France


Kumar Venayagamoorthy

Zita Vale

Juan M Corchado

Tiago Pinto