Optimizing the thermal performance of solar energy devices using meta-heuristic algorithms: A critical review

Afzal A., Buradi A., Jilte R., Shaik S., Kaladgi A. R., ARICI M., ...Daha Fazla

RENEWABLE & SUSTAINABLE ENERGY REVIEWS, cilt.173, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Derleme
  • Cilt numarası: 173
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1016/j.rser.2022.112903
  • Dergi Adı: RENEWABLE & SUSTAINABLE ENERGY REVIEWS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, CAB Abstracts, Communication Abstracts, Compendex, Greenfile, INSPEC, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: Meta-heuristic algorithms, Hybrid algorithm, Solar energy, Optimization, Thermal performance, PARTICLE SWARM OPTIMIZATION, DESICCANT COOLING SYSTEM, ARTIFICIAL BEE COLONY, ASSISTED HEAT-PUMP, MULTIOBJECTIVE OPTIMIZATION, GENETIC ALGORITHM, NEURAL-NETWORK, EXERGOECONOMIC ANALYSIS, DESIGN OPTIMIZATION, NSGA-II
  • Kocaeli Üniversitesi Adresli: Evet

Özet

Since solar energy is intermittent, finding the best solutions for solar operated devices is a challenge. Multiple techniques exist to reach the best solutions for optimal solar operated devices. A thorough review of solar energy systems' optimization methods and tools is presented in this work. The intelligent optimization techniques for solar energy systems are discussed, including their functions, constraints, contributions, mathematical models, and analysis methods. Optimization studies using new and traditional generation techniques are analyzed, and a few optimization methods, including combined hybrid algorithms, are presented. New generation artificial in-telligence algorithms have been most widely used during the last decade, needing less computational time. They have good convergence and better accuracy than traditional optimization methods. They can scan local and global optima and do robust calculations. Solar system optimization has demonstrated remarkable benefits in size, load demand, and electricity output. The improvements reduce operating expenditures, power losses, and peak output integration and controllability. With a 50% rise in power prices, the optimal number of solar col-lectors rises by approximately 25%. However, with adjustment as per optimization techniques, the solar ab-sorption cooling system's maximum thermal efficiency can be increased up to 75%. The present study recommends using two or more algorithms to overcome the curbs of a single algorithm. The main aim of the optimization strategies, according to this assessment, is to reduce capital expenditures, operation and mainte-nance expenses, and emissions while improving system reliability. The paper also briefly describes several solar energy optimization challenges and issues. Lastly, some practical future approaches for establishing a reliable and efficient solar power system are proposed for developing the complex renewable energy-based hybrid system.