Data Mining for Prosumers Aggregation considering the Self-Generation

  • Catarina RibeiroEmail author
  • Tiago Pinto
  • Zita Vale
  • José Baptista
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 620)


Several challenges arrive with electrical power restructuring, liberalized electricity markets emerge, aiming to improve the system’s efficiency while offering new economic solutions. Privatization and liberalization of previously nationally owned systems are examples of the transformations that have been applied. Microgrids and smart grids emerge and new business models able to cope with new opportunities start being developed. New types of players appear, allowing aggregating a diversity of entities, e.g. generation, storage, electric vehicles, and consumers, Virtual Power Players (VPPs) are a new type of player that allows aggregating a diversity of players to facilitate their participation in the electricity markets. A major task of VPPs is the remuneration of generation and services (maintenance, market operation costs and energy reserves), as well as charging energy consumption. The paper proposes a normalization method that supports a clustering methodology for the remuneration and tariffs definition. This model uses a clustering algorithm, applied on normalized load values, the value of the micro production, generated in the bus associated to the same load, was subtracted from the value of the consumption of that load. This calculation is performed in a real smart grid on buses with associated micro production. This allows the creation of sub-groups of data according to their correlations. The clustering process is evaluated so that the number of data sub-groups that brings the most added value for the decision making process is found, according to players characteristics.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1. L. Meeus, et al., “Development of the Internal Electricity Market in Europe”, The Electricity Journal, vol. 18, no. 6, pp. 25–35, 2005Google Scholar
  2. 2. I. Praça, C. Ramos, Z. Vale, M. Cordeiro, “MASCEM: A Multi-Agent System that Simulates Competitive Electricity Markets”, IEEE Int. Systems, 18,6,54–60, 2003Google Scholar
  3. 3. V. Koritarov, “Real-World Market Representation with Agents: Modeling the Electricity Market as a Complex Adaptive System with an Agent-Based Approach”, IEEE Power & Energy magazine, pp. 39–46, 2004Google Scholar
  4. 4. M. Shahidehpour, et al., “Market Operations in Electric Power Systems: Forecasting, Scheduling, and Risk Management”, Wiley-IEEE Press, pp. 233–274, 2002Google Scholar
  5. 5. Blumsack S and Fernandez A. “Ready or not, here comes the smart grid!” Energy. 2012; 37(1):61–8Google Scholar
  6. 6. Sousa, T. et al., “Intelligent Energy Resource Management Considering Vehicle-to-Grid: A Simulated Annealing Approach,” IEEE Trans. on Smart Grid, 3, 535–542, 2012Google Scholar
  7. 7. Z. Vale, T. Pinto, I. Praça, H. Morais, “MASCEM - Electricity markets simulation with strategically acting players”, IEEE Intelligent Systems, vol. 26, n. 2, Special Issue on AI in Power Systems and Energy Markets, 2011Google Scholar
  8. 8. T. Pinto, et al, “Multi-Agent Based Electricity Market Simulator With VPP: Conceptual and Implementation Issues”, 2009 IEEE PES General Meeting, 2009Google Scholar
  9. 9. Oliveira, P., “MASGriP - A Multi-Agent Smart Grid Simulation Plataform,” IEEE 2012 - Power and Energy Society General Meeting, San Diego, USA, 2012, pp. 1–10Google Scholar
  10. 10. Pinto, T.,, “Adaptive Learning in Agents Behaviour: a Framework for Electricity Markets Simulation,” Integr. Comput. Aided. Eng., vol. 21, no. 4, pp. 399–415, 2014Google Scholar
  11. 11. C. Ribeiro., et al., “Data Mining approach for Decision Support in real data based Smart Grid scenario” IATEM, 2015Google Scholar
  12. 12. Ribeiro C., et al., “Intelligent Remuneration and Tariffs in for Virtual Power Players”, IEEE PowerTech (POWERTECH) Grenoble, France, 16–20 June, 2013Google Scholar
  13. 13. Anil K. Jain et. al., (1999) “Data Clustering: A Review.” ACM Computing Surveys, 31 (3). pp. 264–323.Google Scholar
  14. 14. Anil K. Jain, “Data Clustering: 50 years beyond K-Means”. Pattern Recognition Letters, Elsevier, Vol. 31, Issue 8, pp.651–666, June 2010.Google Scholar
  15. 15. Chicco et al., “Support Vector Clustering of Electrical Load Pattern Data”. IEEE Transactions on Power Systems, vol.24, no.3, pp.1619–1628, August 2009.Google Scholar
  16. 16. Canizes B. et. al., “Resource Scheduling in Residential Microgrids Considering Energy Selling to External Players”, Power Systems Conference (PSC 2015), South Carolina, USA, 10-13 March, 2015Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Catarina Ribeiro
    • 1
    • 2
    Email author
  • Tiago Pinto
    • 1
  • Zita Vale
    • 1
  • José Baptista
    • 2
  1. 1.GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Institute of EngineeringPolytechnic of Porto (ISEP/IPP)PortoPortugal
  2. 2.UTAD – Universidade de Trás-os-Montes e Alto-DouroVila RealPortugal

Personalised recommendations