DocumentCode
1787448
Title
Prosumers as Aggregators in the DEZENT Context of Regenerative Power Production
Author
Montanari, Ugo ; Siwe, Alain Tcheukam
Author_Institution
Dept. of Comput. Sci., Univ. of Pisa, Pisa, Italy
fYear
2014
fDate
8-12 Sept. 2014
Firstpage
167
Lastpage
174
Abstract
Captive consumers of the current traditional and centralized power management systems will become proactive in the future of the smart grid. Their flexibilities will allow them to become prosumers. A prosumer (producer-consumer) is defined as a user that not only consumes electricity, but can also produce and store electricity. A new concept of aggregator has been introduced in the power market. The aggregator exploits the active participation of prosumers in order to provide commercial service in the power market. In this paper, we focus on power market models in which prosumers interact in a distributed environment during the purchase or sale of electric power. We propose a new aggregator which operates in the DEZENT power market model. The aggregator consists of a collection of prosumers who make use of reinforcement learning and of optimization techniques for the planning phase of their electricity production and consumption. In the paper we discuss the assumptions on which the aggregator design is based and we compare its behaviour with that of the aggregator proposed in the EU ADDRESS projects.
Keywords
learning (artificial intelligence); optimisation; power consumption; power engineering computing; power markets; smart power grids; DEZENT context; EU ADDRESS projects; aggregators; captive consumers; centralized power management systems; distributed environment; electric power; electricity consumption; electricity production; optimization techniques; power market; producer-consumer; prosumers; regenerative power production; reinforcement learning; smart grid; Electricity; Learning (artificial intelligence); Optimization; Power markets; Production; Sociology; Statistics; Distributed and real time systems; Optimization; Power markets; Reinforcement learning; Renewable energy sources; Smart power grid;
fLanguage
English
Publisher
ieee
Conference_Titel
Self-Adaptive and Self-Organizing Systems (SASO), 2014 IEEE Eighth International Conference on
Conference_Location
London
Type
conf
DOI
10.1109/SASO.2014.30
Filename
7001013
Link To Document