Abstract
A new approach to solve the multiobjective reactive power compensation (RPC) problem is presented. It is based on the combination of genetic algorithm (GA) and the
ɛ-dominance concept. The algorithm maintains a finite-sized archive of nondominated solutions (Pareto solution) which gets iteratively updated in the presence of new solutions based on the concept of
ɛ-dominance. The use of
ɛ-dominance makes the algorithms practical by allowing a decision maker (DM) able to control the resolution of the Pareto set approximation according to his needs. The proposed approach is suitable to RPC problem where the objective functions may be ill-defined and having nonconvex Pareto-optimal front. It gives a reasonable freedom in choosing compensation devices from the available commercial devices. It may save computing time in cases of small archive.
Moreover to help the DM to extract the best compromise solution from a finite set of alternatives a TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) method is adopted. It is based upon simultaneous minimization of distance from an ideal point (IP) and maximization of distance from a nadir point (NP).
The proposed approach is carried out on the standard IEEE 30-bus 6-generator test system. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto-optimal nondominated solutions of the multiobjective RPC problem in one single run.