AQUINO, R. R. B.; http://lattes.cnpq.br/0731639653204720; AQUINO, Ronaldo Ribeiro Barbosa de.
Résumé:
This work dcals with the study of artificial neural networks(ANN) to solve optmization
problems and their applications in the mid-tcrm operation planning of hydrothermal
gencration systcms. The operation planning problem deals with economic power
dispatches, that is, with the scheduling of hydro and thermal power plants that
minimizes the overall production cost while satisfies the load demmand. The study of
ANN as optmization tools for solving large scale problems was motivated by the
necessity to being up to date with the state of the art of this new technology. This
technique has a great potential for hardware VLSI implementation, in which could be
more efficient then traditional optimization techniques. The operation planning of
hydrothermal generation systems is a large scale problem, whose complexity increases
as the planning horizon increases and the aceuracy of the system modeling increases.
Hence, to solve such a large problem an efficient optimization technique is always
necessary. This work considers recurrents ANN to solve linear and quadratic
programming problems. These networks are based on the solution of a set of differential
equations that are obtained from a transformation of a Lagrangian energy function. This
network also provides the corresponding Lagrange multiplier associated with each
constraint, which is the marginal price.
The ANN was applied to solve the economic power dispatches of the
CHESF/ELETRONORTE interconnected system to which was calculated the
optimized solution, the marginal costs and the water values associated with each hydro
plant