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

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TLDR
In this article, the impact assessment of optimally placed different types of distributed generations (DGs) 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.
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.

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Optimal DG placement by multi-objective opposition based chaotic differential evolution for techno-economic analysis

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References
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Journal ArticleDOI

Distributed generation : a definition

TL;DR: In this article, the relevant issues and aims at providing a general definition for distributed power generation in competitive electricity markets are discussed, which can be defined as electric power generation within distribution networks or on the customer side of the network.
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Distributed generation: definition, benefits and issues

TL;DR: In this paper, the authors start from the observation that there is a renewed interest in small-scale electricity generation and then move on with a discussion of the major benefits and issues of Small-Scale Electricity Generation.
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Analytical approaches for optimal placement of distributed generation sources in power systems

TL;DR: In this article, the optimal location to place a DG in radial as well as networked systems to minimize the power loss of the system has been investigated to obtain the maximum potential benefits.
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Summary of Distributed Resources Impact on Power Delivery Systems

TL;DR: Several system issues which may be encountered as DR penetrates into distribution systems are discussed, including vulnerability and overvoltages due to islanding and coordination with reclosing, system restoration and network issues.
Journal ArticleDOI

A multiobjective evolutionary algorithm for the sizing and siting of distributed generation

TL;DR: In this article, a multiobjective formulation for the siting and sizing of DG resources into existing distribution networks is proposed, which permits the planner to decide the best compromise between cost of network upgrading, cost of power losses, and cost of energy not supplied.
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