The Hybrid Harris Hawks Optimizer-Arithmetic Optimization Algorithm: A New Hybrid Algorithm for Sizing Optimization and Design of Microgrids

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ÇETİNBAŞ İ., TAMYÜREK B., DEMİRTAŞ M.

IEEE Access, cilt.10, ss.19254-19283, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 10
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1109/access.2022.3151119
  • Dergi Adı: IEEE Access
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Sayfa Sayıları: ss.19254-19283
  • Anahtar Kelimeler: Arithmetic optimization algorithm, Harris hawks optimizer, hybrid algorithm, Friedman ranking test, microgrid, off-grid, optimal capacity planning, sizing optimization, Wilcoxon signed rank test, RENEWABLE ENERGY SYSTEM, TECHNOECONOMIC ANALYSIS, POWER, BATTERY, STORAGE, WIND, COST, MODEL
  • Gazi Üniversitesi Adresli: Evet

Özet

AuthorThis paper presents a new hybrid metaheuristic algorithm, the hybrid Harris Hawks Optimizer-Arithmetic Optimization Algorithm (hHHO-AOA), as we have named it. It is proposed for sizing optimization and design of autonomous microgrids. The proposed hybrid algorithm has been developed based on operating the Harris Hawks Optimizer (HHO) and the Arithmetic Optimization Algorithm (AOA) in a uniquely cooperative manner. The developed algorithm is expected to increase the solution accuracy by increasing the solution diversity during an optimization process. The performance is verified with the evaluation metrics and the well-known statistical tests. According to the Friedman ranking test, the new algorithm performs 77.9% better than HHO and 78.6% better than AOA. Similarly, the performance checked with the Wilcoxon signed-rank test has revealed a significant superiority in solution accuracy compared to HHO and AOA alone. Later, the hybrid algorithm is tested on a microgrid that consists of a photovoltaic (PV) system, a wind turbine (WT) system, a battery energy storage system (BESS), diesel generators (DGs), and a commercial type load. For the optimal capacity planning of these components, a problem in which the loss of power supply probability (LPSP) and the cost of energy (COE) are defined as the objective function is formulated. The optimization done by the proposed algorithm has produced the lowest LPSP and the COE along with the highest rate of renewable fraction (RF). In conclusion, it is demonstrated that the new hHHO-AOA has proved itself in designing reliable, economical, and eco-friendly autonomous microgrids in the best optimal way.