An integrated approach to economic emission load dispatching using neural network and goal-attainment methods

This paper presents an integrated approach comprising artificial neural network (ANN) and goal-attainment (GA) methods to economic emission load dispatching (EELD) in power system operation and scheduling phases. The GA method, which is one of the most powerful tools for multiobjective optimization problems, is quantitatively performed to grasp trade-off relations between the two conflicting objectives (economy and emission impact) in the training set creation phase. Finally, a radial basis function ANN is trained by the orthogonal least squares learning algorithm to reach the optimal generations. The ANN models so developed have been tested to solve EELD problem on B-bus and 71-bus power systems. The test results demonstrate that the proposed approach is capable of obtaining well-coordinated optimal solutions suitable both in accuracy and speed while allowing flexibility in the operation of the generators.