Using learning automata for multi-objective generation dispatch considering cost, voltage stability and power losses

The economical and secure operation of power systems has significant importance. Due to technical limitations, the best economical operation point is not always the desired operating point for system stability or power losses. In this study, first, the most economical operating point is obtained by solving the nonlinear, network-constrained economic dispatch problem using a genetic algorithm. Then, the system voltage stability is analyzed to compare the different possible operating points using V-Q sensitivity analysis. The power losses, obtained for various operating points, are considered the third objective function. Finally, these 3 aspects of cost, voltage stability, and power losses are combined, using the learning automata technique, to achieve a multi-objective optimization solution. The methodology was implemented in MATLAB 7.8 and applied to the IEEE 30-bus test system. The same technique of learning automata may be applied in the future to similar problems that need multi-objective consideration.