Development of new tools to promote a more effective consumer participation in short-term electricity markets

Abstract

This paper summarizes the research work performed to show the capability of a combination of tools based on Self-Organizing Maps (SOM) and Physically Based Load Models (PBLM) to classify and extract pat-terns from distributor, aggregator and customer electrical demand databases (the objective known as data mining). This approach basically uses low cost information avail-able for almost all supply side agents: historic load curves of several kinds of customers. The first objective is to find a correlation between demand and the evolution of energy prices in short-term energy markets. A SOM was trained that should allow to select the most suitable customer clusters whose demand modification would benefit cus-tomer and supply-side agents through, for example, energy efficiency, distributed generation or demand response. After a previous evaluation through PBLM of different possible strategies to reduce demand during consumption peaks, a SOM was trained to detect opportunities among users with high reduction capabilities during periods when day-ahead prices are lower than shorter-term prices. The results obtained clearly show the suitability of SOM ap-proach to find easily coherent clusters between electrical users with high demand or available response capacity, and therefore a possible way to promote customer partici-pation in electrical energy markets is opened.