GA-based optimization for optimally placed and properly coordinated control of distributed generations and Static Var Compensator in distribution networks

Abstract

This paper presents the impact assessment of distributed generations (DGs) (such as T2and T4i.e operating at different power factors such as 0.85, 0.90, 0.95 and 0.99 leading and lagging, respectively) and flexible alternating current transmission systems (FACTS) controllers like SVC with different load models (DLMs) in distribution power system networks by using genetic algorithms (GA) from minimum total MVA intake of main substation viewpoint. The different type DGs (such as T2and T4i.e. operating at different power factors such as 0.85, 0.90, 0.95 and 0.99 leading and lagging, respectively) and FACTS controller like SVC with DLMs are considered by employing GA in DPSs form minimum total MVA intake of main substation viewpoint. Different distribution power system (DPS) performance indices viewpoint, such as minimization of real power loss, minimization of reactive power loss, improvement of voltage profile, reduce the short circuit current or MVA line capacity and reduce the environmental greenhouse gases like carbon dioxide (CO2), sulfur dioxide (SO2), nitrogen oxide (NOx) and particulate matters in an emergency e.g. under fault, sudden change in field excitation of alternators or load increase in DPSs are considered. This paper also investigated the comparisons of different DGs (such as T2and T4) and FACTS controller like SVC with DLMs by employing GA in DPSs form minimum total MVA intake of main substation viewpoint. The effectiveness of the proposed methodology is tested on IEEE 37-bus distribution test system. All the simulations are done on MATLAB package toolbox. Keywords: Different load models (DLMs), Distributed generations (DGs), Static Var Compensator (SVC), Distributed generation planning, Genetic algorithm (GA), Different load modes (DLMs), DPS performance indices