JOURNAL ARTICLE

Sustainable energy optimization in dual-area systems: Integrating floating solar with hydroelectric power using hCIGWO and advanced 2-DOF controllers.

  • Published In: Journal of Renewable & Sustainable Energy, 2025, v. 17, n. 1. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Gupta, Rohan Kumar; Shankar, Ravi; Kumar, Amitesh 3 of 3

Abstract

This article focuses on enhancing frequency regulation in interconnected renewable energy systems by integrating floating solar panels with hydroelectric power at the Omkareshwar Hydroelectric plant in India. It proposes a novel hybrid Circle-Inspired Gray Wolf Optimization (hCIGWO) algorithm to optimize a two-degree-of-freedom proportional integral derivative controller cascaded with fractional order integral and proportional integral controllers (2-DOF PIDFOI-PI), addressing stability challenges due to renewable intermittency. The study validates the proposed controller’s superior performance over existing optimization methods across five disturbance scenarios, demonstrating up to 81% reduction in settling time and significant error minimization, while confirming system stability through gain and phase margin analysis. Additionally, the paper assesses the potential for integrating a 600 MW floating solar plant and a 560 kW wind plant near the hydro facility, suggesting future research on economic feasibility and inclusion of other renewable sources.

Additional Information

  • Source:Journal of Renewable & Sustainable Energy. 2025/01, Vol. 17, Issue 1, p1
  • Document Type:Article
  • Subject Area:Power and Energy
  • Publication Date:2025
  • ISSN:1941-7012
  • DOI:10.1063/5.0234422
  • Accession Number:183417721
  • Copyright Statement:Copyright of Journal of Renewable & Sustainable Energy is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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