DocumentCode :
1361543
Title :
Neural networks approach for solving economic dispatch problem with transmission capacity constraints
Author :
Yalcinoz, T. ; Short, M.J.
Author_Institution :
Dept. of Electr. & Electron. Eng., Imperial Coll. of Sci., Technol. & Med., London, UK
Volume :
13
Issue :
2
fYear :
1998
fDate :
5/1/1998 12:00:00 AM
Firstpage :
307
Lastpage :
313
Abstract :
This study presents a new approach using Hopfield neural networks for solving the economic dispatch (ED) problem with transmission capacity constraints. The proposed method is based on an improved Hopfield neural network which was presented by Gee et al. (1994). The authors discussed a new mapping technique for quadratic 0-1 programming problems with linear equality and inequality constraints. The special methodology improved the performance of Hopfield neural networks for solving combinatorial optimization problems. The authors have now modified Gee and Prager´s (GP) method in order to solve ED with transmission capacity constraints. Constraints are handled using a combination of the GP model and the model of Abe et al. (1992). The proposed method (PHN) has achieved efficient and accurate solutions for two-area power systems with 3, 4, 40 and 120 units. The PHN results are very close to those obtained using the quadratic programming method
Keywords :
Hopfield neural nets; combinatorial mathematics; economics; load dispatching; power system analysis computing; power system interconnection; power system planning; quadratic programming; transmission network calculations; Hopfield neural networks; combinatorial optimization problems; mapping technique; planning optimisation; power system economic dispatch problem; quadratic 0-1 programming problems; transmission capacity constraints; two-area power systems; Costs; Environmental economics; Hopfield neural networks; Linear programming; Neural networks; Power generation economics; Power system analysis computing; Power system economics; Power system modeling; Production systems;
fLanguage :
English
Journal_Title :
Power Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
0885-8950
Type :
jour
DOI :
10.1109/59.667341
Filename :
667341
Link To Document :
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