DocumentCode :
662373
Title :
An enhancement of agent-based power supply-demand matching by using ANN-based forecaster
Author :
Maruf, M.N.I. ; Hurtado Munoz, L.A. ; Nguyen, P.H. ; Lopes Ferreira, H.M. ; Kling, W.L.
Author_Institution :
Dept. of Electr. Eng., Eindhoven Univ. of Technol. (TU/e), Eindhoven, Netherlands
fYear :
2013
fDate :
6-9 Oct. 2013
Firstpage :
1
Lastpage :
5
Abstract :
Local supply-demand matching in power grids by means of advanced information and communication technology (ICT) is emerging due to the increasing integration of distributed energy resources (DER). Although advantages of the local matching mechanism have been proved by either research works or demonstrations, there are some difficulties on being proactive to handle uncertainty from renewable energy sources (RES) and new types of load consumption. This paper aims to enhance the matching mechanism using multi-agent systems (MAS) and artificial neural network (ANN) to investigate and determine DER´s flexibility to compensate that uncertainty. Under a more general platform for smart grid functions, this paper presents a model to achieve a match between the forecasted supply and demand. Short-term forecasting based on an ANN model is used to predict the stochastic behavior of weather data. The model considers various scenarios and the potential from household demand side management. The results from the performed simulations indicate feasible DER´s flexibility for power matching, which can be further adapted for different scenarios to serve local or more grid-related optimization objectives.
Keywords :
demand side management; load forecasting; neural nets; power engineering computing; renewable energy sources; smart power grids; ANN based forecaster; ICT; artificial neural network; distributed energy resources; grid related optimization objective; household demand side management; information and communication technology; load consumption; local matching mechanism; local supply demand matching; multiagent system; power grid; power matching; power supply demand matching; renewable energy source; short term forecasting; smart grid function; stochastic behavior; weather data; Adaptation models; Artificial neural networks; Data models; Forecasting; Load modeling; Mathematical model; Smart grids; Artificial Neural Networks; Distributed Energy Resources; Multi-Agent Systems; Renewable Energy Sources; Supply-Demand Matching;
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.2013.6695257
Filename :
6695257
Link To Document :
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