Genetic algorithm for impact assessment of optimally placed distributed generations with different load models from minimum total MVA intake viewpoint of main substation

Bindeshwar Singh, V. Mukherjee and Prabhakar Tiwari

Renewable and Sustainable Energy Reviews, 2016, vol. 57, issue C, 1611-1636

Abstract: This paper presents the impact assessment of optimally placed different types of distributed generations (DGs) such as DG-1(T1), DG-2 (T2), DG-3 (T3), and DG-4 (T4) with different load models (DMLs) by using genetic algorithm (GA) in distribution power systems (DPSs) from minimum total mega volt ampere (MVA) intake viewpoint of main substation. This paper also presents the impact assessment of optimally placed same kind of DGssuch as DG-2(T2) and DG-4(T4) operating at different power factors (varies from 0.80 to 0.99 leading and lagging, respectively) with DLMs by GA in DPSs from minimum total MVA intake viewpoint of main substation. Different power system (PS) performance indices such as minimization of real power loss, minimization of reactive power loss, improvement of voltage profile, reduction of short circuit current or MVA line capacity and reduction in the emission of environmental greenhouse gases (GHG) such as carbon dioxide (CO2), sulphur dioxide (SO2), nitrogen oxide (NOx) and particulate matters and in emergency like conditions such as under fault, sudden change in field excitation of alternators or load increased in DPSs are calculated. The effectiveness of the proposed methodology is illustrated on IEEE-37 bus distribution test system. This research article is very much useful for practitioners working on the implementation of renewable and building of future electricity grids and also includes the different PS performance indicators from better social welfare, reduced in the environmental pollutants emission, improved the technical issues, reduced the economical burden, and betters the security viewpoints.

Keywords: Different load models (DLMs); Distributed generations (DGs); Distributed generation planning; Genetic algorithm (GA); Power flow analysis; Power system performance Indices (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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DOI: 10.1016/j.rser.2015.12.204

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