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Moazzami Gudarzi, Amirhosein, Ozgoli, Hassan Ali. (1403). Optimal Selection and Efficient Utilization of Particle Swarm Optimization Methods for Designing Renewable Energy Microgrids. پژوهشنامه مدیریت اجرایی, 1(2), 20-30. doi: 10.22080/cste.2024.27781.1002
Amirhosein Moazzami Gudarzi; Hassan Ali Ozgoli. "Optimal Selection and Efficient Utilization of Particle Swarm Optimization Methods for Designing Renewable Energy Microgrids". پژوهشنامه مدیریت اجرایی, 1, 2, 1403, 20-30. doi: 10.22080/cste.2024.27781.1002
Moazzami Gudarzi, Amirhosein, Ozgoli, Hassan Ali. (1403). 'Optimal Selection and Efficient Utilization of Particle Swarm Optimization Methods for Designing Renewable Energy Microgrids', پژوهشنامه مدیریت اجرایی, 1(2), pp. 20-30. doi: 10.22080/cste.2024.27781.1002
Moazzami Gudarzi, Amirhosein, Ozgoli, Hassan Ali. Optimal Selection and Efficient Utilization of Particle Swarm Optimization Methods for Designing Renewable Energy Microgrids. پژوهشنامه مدیریت اجرایی, 1403; 1(2): 20-30. doi: 10.22080/cste.2024.27781.1002

Optimal Selection and Efficient Utilization of Particle Swarm Optimization Methods for Designing Renewable Energy Microgrids

Contributions of Science and Technology for Engineering
دوره 1، شماره 2، شهریور 2024، صفحه 20-30    اصل مقاله (491.65 K)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22080/cste.2024.27781.1002
نویسندگان
Amirhosein Moazzami Gudarzi1؛ Hassan Ali Ozgoli* 2
1Industrial Engineering Department, University of Tor Vergata, Rome, Italy
2School of Engineering, Macquarie University, Sydney, NSW 2109, Australia
تاریخ دریافت: 26 فروردین 1403،  تاریخ بازنگری: 04 اردیبهشت 1403،  تاریخ پذیرش: 27 اردیبهشت 1403 
چکیده
In recent years, renewable energy sources have gained significant attention. Optimizing small-scale renewable energy systems plays a crucial role in the effective and economical use of these resources. Particle Swarm Optimization (PSO) is a popular stochastic optimization method widely applied in various fields. However, standard PSO techniques face challenges, including high computational complexity and rapid convergence rates. This study presents a modified PSO, Comprehensive Learning Particle Swarm Optimization (CLPSO), and Generalized PSO (GEPSO) techniques to optimize the capacity sizing of hybrid power generation systems. These systems include photovoltaic (PV), wind, and battery units to supply power to an Information and Communication Technology (ICT) center. The research evaluates two scenarios: a standalone system with PV, wind, and battery units, and a grid-connected system with PV and wind units. Results demonstrate that the CLPSO technique significantly reduces overall investment costs compared to standard PSO, MPSO, and GEPSO algorithms, by 53.34% and 27.28% for standalone and grid-connected systems, respectively. Furthermore, CLPSO reduces computation time by 57.9% in grid-connected systems and improves energy procurement efficiency, decreasing the required energy purchased from the grid by up to 11.84%. Ultimately, CLPSO outperforms other PSO techniques in terms of both precision and efficiency, making it the most suitable method for solving optimization problems in renewable microgrid design.
کلیدواژه‌ها
Microgrid؛ Optimal Microgrid Design؛ RRenewable Energy Resources؛ Distributed Generation Units؛ Particle Swarm Optimization
مراجع
  • Ozgoli, H. A., Safari, S., & Sharifi, M. H. (2020). Integration of a biomass-fueled Proton Exchange Membrane Fuel Cell system and a Vanadium Redox Battery as a power generation and storage system. Sustainable Energy Technologies and Assessments, 42, 100896. doi:10.1016/j.seta.2020.100896.
  • Safari, S., Ghasedi, A. H., & Ozgoli, H. A. (2021). Integration of solar dryer with a hybrid system of gasifier-solid oxide fuel cell/micro gas turbine: Energy, economy, and environmental analysis. Environmental Progress & Sustainable Energy, 40(3), 13569. doi:10.1002/ep.13569.
  • Muhammad Arif, S., Hussain, A., Tjing Lie, T., Muhammad Ahsan, S., & Abbas Khan, H. (2020). Analytical Hybrid Particle Swarm Optimization Algorithm for Optimal Siting and Sizing of Distributed Generation in Smart Grid. Journal of Modern Power Systems and Clean Energy, 8(6), 1221–1230. doi:10.35833/mpce.2019.000143.
  • Dileep, G. (2020). A survey on smart grid technologies and applications. Renewable Energy, 146, 2589–2625. doi:10.1016/j.renene.2019.08.092.
  • Ozgoli, H. A., & Ghadamian, H. (2016). Energy price analysis of a biomass gasification-solid oxide fuel cell-gas turbine power plant. Iranian Journal of Hydrogen & Fuel Cell, 1(1), 45–58.
  • Karunarathne, E., Pasupuleti, J., Ekanayake, J., & Almeida, D. (2020). Comprehensive learning particle swarm optimization for sizing and placement of distributed generation for network loss reduction. Indonesian Journal of Electrical Engineering and Computer Science, 20(1), 16–23. doi:10.11591/ijeecs.v20.i1.pp16-23.
  • Eltamaly, A. M., Ali, E., Bumazza, M., Mulyono, S., & Yasin, M. (2021). Optimal Design of Hybrid Renewable Energy System for a Reverse Osmosis Desalination System in Arar, Saudi Arabia. Arabian Journal for Science and Engineering, 46(10), 9879–9897. doi:10.1007/s13369-021-05645-0.
  • Sedighizadeh, D., Masehian, E., Sedighizadeh, M., & Akbaripour, H. (2021). GEPSO: A new generalized particle swarm optimization algorithm. Mathematics and Computers in Simulation, 179, 194–212. doi:10.1016/j.matcom.2020.08.013.
  • Jirdehi, M. A., Tabar, V. S., Ghassemzadeh, S., & Tohidi, S. (2020). Different aspects of microgrid management: A comprehensive review. Journal of Energy Storage, 30, 101457. doi:10.1016/j.est.2020.101457.
  • Singh, A., & Surjan, B. S. (2014). Microgrid: A review. International Journal of Research in Engineering and Technology, 3(2), 185-198. doi:10.15623/ijret.2014.0302033.
  • Parhizi, S., Lotfi, H., Khodaei, A., & Bahramirad, S. (2015). State of the art in research on microgrids: A review. IEEE Access, 3, 890–925. doi:10.1109/ACCESS.2015.2443119.
  • Obara, S. (2014). Optimum Design of Renewable Energy Systems. Advances in Environmental Engineering and Green Technologies, IGI Global, Hershey, United States. doi:10.4018/978-1-4666-5796-0.
  • Wang, Z., Chen, B., Wang, J., Kim, J., & Begovic, M. M. (2014). Robust optimization based optimal DG placement in microgrids. IEEE Transactions on Smart Grid, 5(5), 2173–2182. doi:10.1109/TSG.2014.2321748.
  • Sheng, W., Liu, K., Meng, X., Ye, X., & Liu, Y. (2015). Research and practice on typical modes and optimal allocation method for PV-Wind-ES in Microgrid. Electric Power Systems Research, 120, 242–255. doi:10.1016/j.epsr.2014.02.011.
  • Cao, Y., Zhang, H., Li, W., Zhou, M., Zhang, Y., & Chaovalitwongse, W. A. (2019). Comprehensive Learning Particle Swarm Optimization Algorithm with Local Search for Multimodal Functions. IEEE Transactions on Evolutionary Computation, 23(4), 718–731. doi:10.1109/TEVC.2018.2885075.
  • Zia, M. F., Elbouchikhi, E., & Benbouzid, M. (2018). Microgrids energy management systems: A critical review on methods, solutions, and prospects. Applied Energy, 222, 1033–1055. doi:10.1016/j.apenergy.2018.04.103.
  • Hatziargyriou, N. (2014). Microgrids: architectures and control. John Wiley & Sons, Hoboken, United States.
  • Hassan, A., Saadawi, M., Kandil, M., & Saeed, M. (2015). Modified particle swarm optimisation technique for optimal design of small renewable energy system supplying a specific load at Mansoura University. IET Renewable Power Generation, 9(5), 474–483. doi:10.1049/iet-rpg.2014.0170.
  • Kumar, P. S., Chandrasena, R. P. S., Ramu, V., Srinivas, G. N., & Babu, K. V. S. M. (2020). Energy Management System for Small Scale Hybrid Wind Solar Battery Based Microgrid. IEEE Access, 8, 8336–8345. doi:10.1109/access.2020.2964052.
  • Ibrahim, A. W., Shafik, M. B., Ding, M., Sarhan, M. A., Fang, Z., Alareqi, A. G., Almoqri, T., & Al-Rassas, A. M. (2020). PV maximum power-point tracking using modified particle swarm optimization under partial shading conditions. Chinese Journal of Electrical Engineering, 6(4), 106–121. doi:10.23919/CJEE.2020.000035.
  • Ozgoli, H. A., Elyasi, S., & Mollazadeh, M. (2015). Hydrodynamic and electrochemical modeling of vanadium redox flow battery. Mechanics & Industry, 16(2). doi:10.1051/meca/2014071.
  • Abdelshafy, A. M., Hassan, H., & Jurasz, J. (2018). Optimal design of a grid-connected desalination plant powered by renewable energy resources using a hybrid PSO–GWO approach. Energy Conversion and Management, 173, 331–347. doi:10.1016/j.enconman.2018.07.083.
  • Hosseinpour, S., Hosseini, S. A. H. S. M., Mehdipour, R., Hemmasi, A. H., & Ozgoli, H. A. (2020). Energy Modeling and Techno-Economic Analysis of a Biomass Gasification-CHAT-ST Power Cycle for Sustainable Approaches in Modern Electricity Grids. Journal of Renewable Energy and Environment, 7(2), 43–51. doi:10.30501/jree.2020.106780.
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