Solving a real large scale mid-term scheduling for power plants via hybrid intelligent neural networks systems

• Published in: The 2011 International Joint Conference on Neural Networks pp. 785 - 792
• Main Authors: Aquino, R. R. B., Neto, O. N., Lira, M. M. S., Carvalho, M. A.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2011
• Subjects: Artificial neural networks; Convergence; Mathematical model; Optimization; Reservoirs; Wind power generation
• ISBN: 1424496357; 9781424496358
• ISSN: 2161-4393; 2161-4407
• DOI: 10.1109/IJCNN.2011.6033301

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.