DocumentCode :
3494034
Title :
Solving a real large scale mid-term scheduling for power plants via hybrid intelligent neural networks systems
Author :
Aquino, Ronaldo R B ; Neto, Otoni Nóbrega ; Lira, Milde M S ; Carvalho, Manoel A., Jr.
Author_Institution :
Fed. Univ. of Pernambuco (UFPE), Recife, Brazil
fYear :
2011
fDate :
July 31 2011-Aug. 5 2011
Firstpage :
785
Lastpage :
792
Abstract :
This paper deals with an application of Artificial Neural Network (ANN) and a Hybrid Intelligent System (HIS) to solve a large scale real world optimization problem, which is an operation planning of generation system in the mid-term operation. This problem is related to economic power dispatch that minimizes the overall production cost while satisfying the load demand. These kinds of problem are large scale optimization problems in which the complexity increases with the planning horizon and the accuracy of the system to be modeled. This work considers the two-phase optimization neural network, which solves dynamically linear and quadratic programming problems with guaranteed optimal convergence and HIS, which combines ANN and Heuristics Rules (HRs) to boost the convergence speed. This network also provides the corresponding Lagrange multiplier associated with each constraint (marginal price). The results pointed out that the applications of the HIS have turned the implementation of ANN models in software more attractive.
Keywords :
dynamic programming; linear programming; neural nets; power engineering computing; power generation dispatch; power generation economics; power generation planning; power generation scheduling; power plants; pricing; quadratic programming; Lagrange multiplier; convergence speed; dynamically linear programming problem; economic power dispatch; generation system operation planning; heuristics rules; load demand; marginal price; planning horizon; quadratic programming problems; real large scale midterm scheduling; two-phase optimization; Artificial neural networks; Convergence; Mathematical model; Optimization; Reservoirs; Wind power generation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), The 2011 International Joint Conference on
Conference_Location :
San Jose, CA
ISSN :
2161-4393
Print_ISBN :
978-1-4244-9635-8
Type :
conf
DOI :
10.1109/IJCNN.2011.6033301
Filename :
6033301
Link To Document :
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