Optimal power system generation scheduling by multi-objective genetic algorithms with preferences

Power system generation scheduling is an important issue both from the economical and environmental safety viewpoints. The scheduling involves decisions with regards to the units start up and shut down times and to the assignment of the load demands to the committed generating units for minimizing the system operation costs and the emission of atmospheric pollutants. As many other real-world engineering problems, power system generation scheduling involves multiple, conflicting optimization criteria for which there exists no single best solution with respect to all criteria considered. Multi-Objective optimization algorithms, based on the principle of Pareto optimality, can then be designed to search for the set of nondominated scheduling solutions from which the decision maker must a posteriori choose the preferred alternative. On the other hand, often, information is available a priori regarding the preference values of the decision maker with respect to the objectives. When possible, it is important to exploit this information during the search so as to focus it on the region of preference of the Pareto optimal set. In this paper, ways are explored to use this preference information for driving a Multi-Objective Genetic Algorithm towards the preferential region of the Pareto optimal front. Two methods are considered: the first one extends the concept of Pareto dominance by biasing the chromosome replacement step of the algorithm by means of numerical weights which express the decision maker’ s preferences; the second one drives the search algorithm by changing the shape of the dominance region according to linear trade-off functions specified by the decision maker. The effectiveness of the proposed approaches is first compared on a case study of literature, Then, a nonlinear, constrained, two-objective power generation scheduling problem is effectively tackled.