Customized normalization method to enhance the clustering process of consumption profiles

Abstract

The restructuring of electricity markets brought many changes to markets operation. To overcome these new challenges, the study of electricity markets operation has been gaining an increasing importance. With the emergence of microgrids and smart grids, new business models able to cope with new opportunities are being developed. New types of players are also emerging, allowing aggregating a diversity of entities, e.g. generation, storage, electric vehicles, and consumers. The virtual power player (VPP) facilitates their participation in the electricity markets and provides a set of new services promoting generation and consumption efficiency, while improving players’ benefits. The contribution of this paper is a customized normalization method that supports a clustering methodology for the remuneration and tariffs definition from VPPs. To implement fair and strategic remuneration and tariff methodologies, this model uses a clustering algorithm, applied on normalized load values, 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 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.