Optimum sizing of hybrid sustainable and renewable energy systems using a modified harris hawks optimizer
To boost the use of renewable energy sources while maintaining reliability and affordability, Multi-source renewable and sustainable energy systems must be optimally sized. This research introduces a stand-alone metaheuristic algorithm for designing a hybrid sustainable and renewable energy system c...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025028762 |
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| Summary: | To boost the use of renewable energy sources while maintaining reliability and affordability, Multi-source renewable and sustainable energy systems must be optimally sized. This research introduces a stand-alone metaheuristic algorithm for designing a hybrid sustainable and renewable energy system combining Wind turbine, PV and battery system. The main goal is to lower the overall present-day system’s cost at the same time considering the indicator of reliability, which is the loss of power supply probability (LPSP), as a constraint. The developed algorithm resulted from enhancing the recent Harris Hawks Optimizer (HHO). The modified version incorporates a vector that saves the best three solutions and opposition learning to enhance the population diversity and assist the algorithm in jumping out of local optima regions. Three scenarios are presented, the first is modeled by PV/Bat the second one is modeled by WT/Bat while the third one consists of PV/WT/Bat. The studied project is located in Sidi Khattab, Relizane province, Algeria. The results demonstrate that the MHHO outperforms a range of well-known algorithms, among which one can cite the original HHO, Krill Optimization Algorithm (KOA), Red Squirrel Algorithm (RSA), Modified Coati Optimization Algorithm (MCOA), and Generalized Oppositional-based Social Spider Algorithm (GOOSE). Compared to the other algorithms, MHHO demonstrated superior performance in all proposed configuration settings. |
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| ISSN: | 2590-1230 |