Multi-objective differential evolution for optimal power flow

Abstract

In recent days, planning and operation of transmission networks are becoming more complicated as a result of the rapid growth in electricity demand, network integrations and movement toward open electricity markets. Several factors related to financial and political issues slow/delay the reinforcement projects to enhance power transmission networks. Proper operation of such networks requires consideration of several factors such as economical dispatch, system security and emissions. Traditional optimal power flow (OPF) provided a tool to achieve such task and has initially dealt with fuel cost only. Later, other objectives were incorporated into the OPF in the form of single objective. Recently, with the progress in evolutionary optimization techniques, it is possible to deal with the real life multi-objective optimization problems. This has been reflected on the OPF with an aim to formulate it as a true multi-objective optimization problem. In this thesis, a true multi-objective formulation of the OPF problem will be carried out, taking into consideration different operational constraints in order to ensure proper system operation. A multi-objective Differential Evolution based approach has been proposed, developed and successfully implemented to solve the multiobjective OPF. A clustering algorithm is applied to manage the size of the Pareto set. Also, an algorithm based on fuzzy set theory is used to extract the best compromise solution. The proposed approach is simulated using three test systems and the results are compared with the available literature. The results show the effectiveness of the proposed approach in solving true multi-objective OPF and also finding well distrusted Pareto solutions.