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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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  • Singh, Bindeshwar
  • Mukherjee, V.
  • Tiwari, Prabhakar

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.

Suggested Citation

  • Singh, Bindeshwar & Mukherjee, V. & Tiwari, Prabhakar, 2016. "Genetic algorithm for impact assessment of optimally placed distributed generations with different load models from minimum total MVA intake viewpoint of main substation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1611-1636.
    • Handle: RePEc:eee:rensus:v:57:y:2016:i:c:p:1611-1636
    DOI: 10.1016/j.rser.2015.12.204
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    References listed on IDEAS

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    1. Singh, Bindeshwar & Mukherjee, V. & Tiwari, Prabhakar, 2015. "A survey on impact assessment of DG and FACTS controllers in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 846-882.
    2. Smith, David K. & Walters, Godfrey A., 2000. "An evolutionary approach for finding optimal trees in undirected networks," European Journal of Operational Research, Elsevier, vol. 120(3), pages 593-602, February.
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    Cited by:

    1. Singh, Bindeshwar & Sharma, Janmejay, 2017. "A review on distributed generation planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 529-544.
    2. Theo, Wai Lip & Lim, Jeng Shiun & Ho, Wai Shin & Hashim, Haslenda & Lee, Chew Tin, 2017. "Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 531-573.
    3. Vikas Singh Bhadoria & Nidhi Singh Pal & Vivek Shrivastava, 2019. "Artificial immune system based approach for size and location optimization of distributed generation in distribution system," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(3), pages 339-349, June.
    4. Hannan, M.A. & Ali, Jamal A. & Mohamed, Azah & Hussain, Aini, 2018. "Optimization techniques to enhance the performance of induction motor drives: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1611-1626.

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