Customized normalization clustering meth-odology for consumers with heterogeneous characteristics

  • Catarina Ribeiro
    GECAD - Polytechnic of Porto / UTAD – University of Trás-os-Montes e Alto-Douro
  • Tiago Pinto
    GECAD - Polytechnic of Porto tmcfp[at]
  • Zita Vale
    Polytechnic of Porto
  • Jose Baptista
    University of Trás-os-Montes e Alto-Douro / CPES - INESCTEC


The increasing use and development of renewable energy sources and distributed generation, brought several changes to the power system operation. Electricity markets worldwide are complex and dynamic environments with very particular characteristics, resulting from their restructuring and evolution into regional and continental scales, along with the constant changes brought by the increasing necessity for an adequate integration of renewable energy sources. With the eminent implementation of micro grids and smart grids, new business models able to cope with the new opportunities are being developed. Virtual Power Players are a new type of player, which allows aggregating a diversity of entities, e.g. generation, storage, electric vehicles, and consumers, to facilitate their participation in the electricity markets and to provide a set of new services promoting generation and consumption efficiency, while improving players` benefits. This paper proposes a clustering methodology regarding the remuneration and tariff of VPP. It proposes a model to implement fair and strategic remuneration and tariff methodologies, using a clustering algorithm, applied to load values, submitted to different types of normalization process, which creates 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 the players characteristics. The proposed clustering methodology has been tested in a real distribution network with 30 bus, including residential and commercial consumers, photovoltaic generation and storage units
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Ribeiro, C., Pinto, T., Vale, Z., & Baptista, J. (2018). Customized normalization clustering meth-odology for consumers with heterogeneous characteristics. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 7(2), 53–69.


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