Improved multi-objective particle swarm optimization with preference strategy for optimal DG integration into the distribution system

Considering the different requirements for decision and state variables in engineering optimizations, an improved multi-objective particle swarm optimization with preference strategy (IMPSO-PS) is presented and applied to the optimal integration of distributed generation (DG) into the distribution system. Preference factors are introduced to quantify the degree of preference for certain attributes in the constraint-space. In addition to the application of a popular non-dominated sorting technique for identifying Pareto solutions, the performance of IMPSO-PS is strengthened via the inclusion of a dynamic selection of the global bests, a novel circular non-dominated selection of particles from one iteration to the next and a special mutation operation. The proposed algorithm has been successfully applied to benchmark functions and to the multi-objective optimal integration of DG into an IEEE 33-bus system. This real-world application aims to satisfy some special preferences and determine the optimal locations and capacities of DG units to minimize the total active power loss of the system and decrease cost caused by power generation and pollutant emissions. The results show that the proposed approach can provide a wider range of Pareto solutions of high quality, while satisfying special preference demands.