An augmented Lagrange programming optimization neural network for short-term hydroelectric generation scheduling

An approach based on augmented Lagrange programming neural networks is proposed for determining the optimal hourly amounts of generated power for the hydro-units in an electric power system. This methodology is based on the Lagrange multiplier theory in optimization and searches for solutions satisfying the necessary conditions of optimality in the state space. The equilibrium point of the network satisfies the Kuhn–Tucker condition for the problem. The equilibrium point of the network corresponds to the Lagrange solution of the problem. The proposed technique has been applied to a multi-reservoir cascaded hydro-electric system with a non-linear power generation function of water discharge rate and storage volume. The water transportation delay between connected reservoirs is also taken into account. Results obtained from this approach are compared with those obtained from the two phase optimization neural network and the conventional augmented Lagrange multiplier method. It is concluded from the results that the proposed method provides better results with respect to constraint satisfaction and is very effective in yielding optimal hydro-generation schedules.