DocumentCode :
662370
Title :
Peak shaving of a heterogeneous cluster of residential flexibility carriers using reinforcement learning
Author :
Claessens, Bert J. ; Vandael, Stijn ; Ruelens, Frederik ; De Craemer, Klaas ; Beusen, B.
Author_Institution :
Flemish Inst. for Technol. Res. VITO, Belgium
fYear :
2013
fDate :
6-9 Oct. 2013
Firstpage :
1
Lastpage :
5
Abstract :
Demand response is often defined as an optimal control problem. However, the practical application is challenged by computational complexity and lack of accurate models and data. In this work we extend upon previous work and combine batch reinforcement learning, using function approximators, with a market-based multi-agent system. The resulting adaptive control strategy is model-free and needs no prior knowledge of the cluster configuration. The strategy is evaluated for two distinct heterogeneous clusters of residential flexibility carriers. The evaluation shows that our self-learning strategy supports effective peak shaving and valley filling within a limited convergence time.
Keywords :
demand side management; electric vehicles; learning (artificial intelligence); multi-agent systems; optimal control; optimisation; adaptive control strategy; cluster configuration; computational complexity; demand response; function approximators; heterogeneous cluster; limited convergence time; market based multiagent system; optimal control problem; peak shaving; reinforcement learning; residential flexibility carriers; self learning strategy; valley filling; Approximation methods; Conferences; Europe; Learning (artificial intelligence); Optimization; Renewable energy sources; Smart grids;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Innovative Smart Grid Technologies Europe (ISGT EUROPE), 2013 4th IEEE/PES
Conference_Location :
Lyngby
Type :
conf
DOI :
10.1109/ISGTEurope.2013.6695254
Filename :
6695254
Link To Document :
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