Development of new tools to promote a more effective consumer participation in short-term electricity markets
Author
Valero Verdú, Sergio; Gabaldón Marín, Antonio; Encinas Redondo, Nuria; Ortiz García, Mario; García Franco, Francisco J.; [et al.]Knowledge Area
Ingeniería EléctricaPublication date
2006-02Publisher
International Computing Sciences Conferences Canadá (ICSC)Bibliographic Citation
VALERO VERDÚ, Sergio et al. Development of new tools to promote a more effective consumer participation in short-term electricity markets. En: International ICSC Symposium on Artificial Intelligence in Energy Systems and Power (1º: 2006: Madeira, Portugal) International ICSC Symposium on Artificial Intelligence in Energy Systems and Power. AIESP 06. Madeira, Portugal, Febrero 7-10 2006. Madeira, Portugal: ICSC Academic Press Canada, 2006. P. 6. ISBN 3-906454-36-3Keywords
Mercados de energía eléctricaPatrón de demanda
Segmentación de clientes
Respuesta de demanda
Modelado de carga
Funcionamiento del sistema de alimentación
Electrical energy markets
Demand pattern
Customer segmentation
Demand Response
Load modeling
Power system operation
Self organizing maps
Auto organización de mapas
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 ...
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