Optimal Sizing of a Hybrid Renewable Photovoltaic-Wind System-Based Microgrid Using Harris Hawk Optimizer

• Published in: International journal of photoenergy Vol. 2022; pp. 1 - 13
• Main Authors: Al-Shamma’a, Abdullrahman A., Hussein Farh, Hassan M., Noman, Abdullah M., Al-Shaalan, Abdullah M., Alkuhayli, Abdulaziz
• Format: Journal Article
• Language: English
• Published: New York Hindawi 23-06-2022; Hindawi Limited
• Subjects: Alternative energy sources; Artificial intelligence; Convergence; Design optimization; Distributed generation; Energy management; Energy resources; Fuel cells; Generators; Genetic algorithms; Heuristic methods; Hybrid systems; Linear programming; Optimization; Optimization algorithms; Oscillations; Photovoltaic cells; Renewable resources; Search algorithms; Sizing; Urban areas; Wind turbines
• ISSN: 1110-662X; 1687-529X
• DOI: 10.1155/2022/4825411

Hybrid renewable energy microgrid has become an attractive solution to electrify urban areas. This research proposes a microgrid design problem including photovoltaic (PV) arrays, wind turbine, diesel, and batteries for which Harris hawk optimization (HHO), a metaheuristic technique, is applied. Based on a long-term techno-economic assessment, the HHO approach is used to determine the best hybrid microgrid size for a community in Saudi Arabia’s northern region. The efficacy of HHO is investigated, and its performance was compared with seven metaheuristic techniques, grasshopper optimization algorithm (GOA), cuckoo search optimizer (CSO), genetic algorithm (GA), Big Bang–Big Crunch (BBBC), coyote optimizer, crow search, and butterfly optimization algorithm (BOA), to attain the HRE microgrid optimal sizing based on annualized system cost (ASC) reduction. Some benchmarks (optimum and worst solutions, mean, median, standard deviation, and rate of convergence) are used to distinguish and analyze the performance of these eight metaheuristic-based approaches. The HHO surpassed the other seven metaheuristic techniques in achieving the best HRE microgrid solution with the lowest ASC (USD 149229.9) followed by GOA (USD 149380.5) and CSO (USD 149382.5). The findings revealed that the HHO, GOA, CSO, and coyote have acceptable performance in terms of capturing the global solution and the speed of convergence, with only minimal oscillations. The BBBC, crow search, GA, and BA, on the other hand, have unacceptably poor performance, trapping to the local solution, oscillations, and a long convergence time. In terms of optimal solution and convergence rate, the BBBC and GA both perform poorly when compared to the other metaheuristic techniques.