Papers by Keyword: Multiobjective Genetic Algorithm

Abstract: In contrast with the traditional distribution expansion planning approach, significant giants can be realized in terms of outage loss, supply quality, and load carrying capacity from the distribution expansion with a suitable size and siting of distributed generation (DG). However, in order to achieve these planning goals, it often requires network upgrades to accommodate the anticipated power flows in the distribution system with DG. To improve utilization and expansion of distribution system, this paper proposes a methodology to derive the optimal strategy of generation expansion and network upgrades including line capacity upgrade, line switches, and network reconfiguration for multiobjective distribution expansion planning by minimizing network investment cost, feeder losses, customer interruption cost, and voltage drop. A multiobjective genetic algorithm (MOGA) is employed to quantify the effects of DG and network upgrades placements on distribution network under load increases and during distribution outages. To demonstrate the effectiveness of the proposed methodology, an IEEE feeder test system is selected for computer simulation to explore the benefits of DG placement for distribution system utilization and expansion.

Abstract: This paper proposes a novel multiobjective genetic algorithm (MOGA), Evaluated Preference Genetic Algorithm (EPGA), for efficiently solving engineering multiobjective optimization problems. EPGA utilizes a preferred objective vector to perform a fast multiobjective ranking schema within a low computation complexity O(MNlogN) where N is the size of genetic population and M is the number of objectives. For verifying the proposed algorithms, this paper studies two engineering problems in which multiple mutual-conflicted objectives should be considered. According to the experimental results, the proposed EPGA can efficiently explore the Pareto front and provide very good solution capabilities for the engineering optimization problems.