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Renewables based dynamic cost-effective optimal scheduling of distributed generators using teaching–learning-based optimization

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  • Swarupa Pinninti

    (GITAM Deemed to be University)

  • Srinivasa Rao Sura

    (GITAM Deemed to be University)

Abstract

Distributed generators (DGs) which may be both renewable energy sources (RES) or conventional fossil fueled generators must be optimally scheduled so as to reduce the generation cost of a power network at the end of the day. Adequate attention must also be given to expand the utilization of RES not only because they are abundant in nature but also because they are clean sources of energy supply. This paper utilizes a robust and efficient teaching–learning-based optimization (TLBO) to optimally schedule the DGs of four dynamic systems so as to reduce their active power generation cost. The cost effective fitness functions of the subject test systems are both linear and non-linear in nature. RES like photovoltaic (PV) systems and wind were prioritized to share the load demand and load profiles of various system were studied. By instigating a savings of 9.36% with 99.9% efficiency, TLBO clearly outperformed a long list of algorithms available in literatures in minimizing the generation cost of the test systems. Various statistical parameters, non-parametric statistical analysis and computational time also points towards the robustness of TLBO in handling any dimensional test system.

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  • Swarupa Pinninti & Srinivasa Rao Sura, 2023. "Renewables based dynamic cost-effective optimal scheduling of distributed generators using teaching–learning-based optimization," 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. 14(1), pages 353-373, March.
    • Handle: RePEc:spr:ijsaem:v:14:y:2023:i:1:d:10.1007_s13198-023-01864-w
    DOI: 10.1007/s13198-023-01864-w
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