Solution of Combined Economic and Emission Dispatch Problem Using a Lagrangian Dynamic Neural Networks

This paper explores the performance of Lagrangian dynamic neural networks (LG-DNN), in solving the combined economic-emission dispatch (CEED) problem. The idea behind the CEED formulation is to estimate the optimal generating unit schedule in such a manner that both the fuel cost and the pollutant emission levels are minimized for a given load demand. The LG-DNN present an elegant approach to transform the CEED optimization problem into dynamical systems whose states converge to the desired optimal generated power configuration. As compared to other neural network approaches, e.g., multilayer perceptron or radial basis function networks, complicated problem mapping or training is not necessary in DNN. Further, the convergence characteristics of LG-DNN are theoretically well established. The proposed approach is investigated in optimizing the CEED of 3 generating unit test systems. Compared to other optimization techniques, the investigated LG-DNN feature a transparent design procedure, lower model complexity, and faster convergence.