Economic emission dispatching with variations of wind power and loads using multi-objective optimization 
by learning automata

Abstract

This paper is concerned with using multi-objective optimization by learning automata (MOLA) for economic 
emission dispatching in the environment where wind power and loads vary. With its capabilities of sequentially 
dimensional search and state memory, MOLA is able to find accurate solutions while satisfying two objectives: 
fuel cost coupled with environmental emission and voltage stability. Its searching quality and efficiency are 
measured using the hypervolume indicator for investigating the quality of Pareto front, and demonstrated by tracking 
the dispatch solutions under significant variations of wind power and load demand. The simulation studies are carried 
out on the modified midwestern American electric power system and the IEEE 118-bus test system, in which wind power 
penetration and load variations present. Evaluated on these two power systems, MOLA is fully compared with multi-objective 
evolutionary algorithm based on decomposition (MOEA/D) and non-dominated sorting genetic algorithm II (NSGA-II). The 
simulation results have shown the superiority of MOLA over NAGA-II and MOEA/D, as it is able to obtain more accurate 
and widely distributed Pareto fronts. In the dynamic environment where the operation condition of both wind speed and 
load demand varies, MOLA outperforms the other two algorithms, with respect to the tracking ability and accuracy of 
the solutions.