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              <text>Chernobyl Disaster Optimizer-Based Optimal Integration of Hybrid Photovoltaic Systems and Network Reconfiguration for Reliable and Quality Power Supply to Nuclear Research Reactors</text>
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              <text>Chernobyl disaster optimizer; D-STATCOM; energy storage system; network reconfiguration; nuclear research reactors; performance; photovoltaic systems; reliability; resilience</text>
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              <text>In view of the complexity and importance of nuclear research reactor (NRR) installations, it is imperative to uphold high standards of reliability and quality in the electricity being supplied to them. In this paper, the performance of low-voltage (LV) distribution feeders integrated with NRRs is improved in terms of reduced distribution loss, improved voltage profile, and reduced greenhouse gas (GHG) emissions by determining the optimal location and size of photovoltaic (PV) systems. In the second stage, the power quality of the feeder is optimized by reducing the total harmonic distortion (THD) by optimally allocating D-STATCOM units. In the third and fourth stages, the reliability and resilience aspects of the feeder are optimized using optimal network reconfiguration (ONR) and by integrating an energy storage system (ESS). To solve the non-linear complex optimization problems at all these stages, an efficient meta-heuristic Chernobyl disaster optimizer (CDO) is proposed. Simulations are performed on a modified IEEE 33-bus feeder considering the non-linear characteristics of NRRs, variability of the feeder loading profile, and PV variability. The study reveals that the proposed methodology can significantly improve the service requirements of NRRs for attaining sustainable research activities.  2024 by the authors.</text>
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              <text>Penubarthi S.R.; Korrapati R.R.; Janamala V.; Nimmagadda C.; Veerendra A.S.; Ravindrakumar S.</text>
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              <text>Modelling, Vol-5, No. 3, pp. 1268-1285.</text>
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              <text>Multidisciplinary Digital Publishing Institute (MDPI)</text>
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              <text>2024-01-01</text>
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              <text>&lt;a href="https://doi.org/10.3390/modelling5030065" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.3390/modelling5030065&lt;/a&gt;
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              <text>All Open Access; Gold Open Access</text>
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              <text>ISSN: 26733951</text>
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              <text>Penubarthi S.R., Department of Electrical and Electronics Engineering, Lakireddy Bali Reddy College of Engineering (Autonomous), Jawaharlal Nehru Technological University, Kakinada (JNTUK), Kakinada, 533003, India; Korrapati R.R., Department of Electrical and Electronics Engineering, R.V.R. &amp;amp; J.C. College of Engineering, Guntur, 522019, India; Janamala V., Department of Electrical and Electronics Engineering, School of Engineering and Technology, Christ University, Bangalore, 560074, India; Nimmagadda C., Department of Electrical and Electronics Engineering, R.V.R. &amp;amp; J.C. College of Engineering, Guntur, 522019, India; Veerendra A.S., Department of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India; Ravindrakumar S., Department of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India</text>
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