GA-based multi-objective optimization for distributed generations planning with DLMs in distribution power systems

Abstract

In the present scenario of all over world, the planning of distributed generations (DGs) in distribution power systems are very important issues from power system performances viewpoints. The broad categories of different types of DGs on the basis of their power delivering characteristics are considered T1, T2, T3 and T4 with different load models (DLMs) for the analysis in this paper. This paper presents the impact assessment of optimally placed different types of DGs (such as T1, T2, T3 and T4) with DLMs by employing genetic algorithm (GA) in the distribution power systems (DPSs) form total minimum real power loss of the system viewpoint. Different DPS performance parameters such as minimization of real power loss, minimization of reactive power loss, improvement of voltage profile, reduction of the short circuit current or MVA line capacity and reduction of the environmental green house gases like carbon dioxide (CO2), sulphur dioxide (SO2), nitrogen oxide (NOx) and particulate matters in emergency e.g. under fault, sudden change in field excitation of alternators or load increase in the distribution power system are considered. The contribution of the present work is to investigate the comparisons of different DGs with DLMs by excercizing GA in the distribution systems form minimum total real power loss of the system viewpoint. The effectiveness of the proposed methodology is tested on IEEE-37 bus distribution test system. The different types of DGs (such as T1, T2, T3 and T4) with DLMs have shown different behaviours for power system performance indices such as PLI, QLI, VDI, SCCI and EIRI viewpoints. The sequence of overall power system performance indices such as PLI, QLI, VDI, SCCI and EIRI are as follows: T2>T1>T4>T3. This paper presents that the overall performance of T2 type DG is better as compared to T1, T3 and T4 types DGs in the distribution system form minimum real power loss of the system viewpoint.