DocumentCode
2366840
Title
Operation of a Multi-Agent System for Load management in smart power distribution system
Author
Biabani, Majid ; Golkar, Masoud Aliakbar ; Sajadi, Amirhossein
Author_Institution
Dept. of Electr. & Comput. Eng., K.N. Toosi Univ. of Technol., Tehran, Iran
fYear
2012
fDate
18-25 May 2012
Firstpage
525
Lastpage
530
Abstract
In this paper a novel approach to accommodate distributed generation resources in the power distribution system is discussed to reduce the peak power demand. Demand dispatch is the capability of aggregate and precisely control individual loads on command. The novel implemented approach in this work is the demand dispatch to demand response. The dispatch algorithms in regularity base are used for controllable loads which can be turned on and off with unnoticeable interruption where the load is forecasted and it is dispatched accordingly by using distributed generation resources and controllable loads, thereby it helps to reduce peak demand. Multi-Agent System (MAS) is consisting a group of agents which are capable of perceived environment that they are located and act on it by communicating with each other to achieve the goals. Therefore a MAs has been adopted to manage the demand dispatch simulation. Load has forecasted in MATLAB and MAS has programmed in ZEUS utilize the forecasted load data to dispatch the load in such a way so as to reduce the peak demand. The agents are located at demand aggregator level, zone level and DG level. They communicate to dispatch the load properly based on resources and load availability.
Keywords
distributed power generation; distribution networks; energy resources; load forecasting; load management; mathematics computing; multi-agent systems; power engineering computing; power generation dispatch; power system management; smart power grids; DG level; MAS operation; MATLAB programming; ZEUS programming; controllable load; demand aggregator level; demand dispatch algorithm; demand response; dispatch simulation; distributed generation resource; load availability; load data forecasting; load management; multiagent system operation; peak power demand reduction; smart power distribution system; zone level; Application software; Biological neural networks; Load forecasting; Load management; Load modeling; Training; Load Management; Multi Agent System; Smart Grid;
fLanguage
English
Publisher
ieee
Conference_Titel
Environment and Electrical Engineering (EEEIC), 2012 11th International Conference on
Conference_Location
Venice
Print_ISBN
978-1-4577-1830-4
Type
conf
DOI
10.1109/EEEIC.2012.6221433
Filename
6221433
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