Vol. 2 No. 2 (2022): Journal of Millimeterwave Communication, Optimization and Modelling
Articles

Techno-Economic Analysis of Grid-Connected PV Systems Using BAT Algorithms and Comparison with other Algorithms: Techno-Economic Analysis of Grid-Connected PV Systems Using BAT Algorithms and Comparison with other Algorithms

Optimization-pdf
  • Abdurazaq Elbaz
Abdurazaq Elbaz
Libyan Center for Solar Energy Research and Studies

Published 31.12.2022

Keywords

  • techno-economic analysis,
  • PV power system,
  • IBAT algorithm,
  • particle swarm optimization,
  • ; whale optimization,
  • Cuckoo Search
    • ...More
    Less

Copyright (c) 2022 Abdurazaq Elbaz

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

This paper proposed a new approach for optimizing and sizing a grid-connected PV system based on an improved algorithm. The novel and improved bat algorithm (IBAT) for optimization was used, which is principled on teaching processes, with a specific aim of minimizing the total net current cost of these systems. There are several techniques, including the very well-known particle swarm optimization (PSO), in addition to the whale optimization algorithm (WOA), and cuckoo search (CS), that are commonly used to handle this optimization. However, to maximize productivity, novel approaches are required. Optimized grid-connected PV systems, also in countries where fossil fuel is abundant, can reduce production expenses. The grid-connected PV system's net current cost (NPC) and energy cost (COE) are more competitive at $19595 and $0.134/kWh, respectively. The COE and NPC were calculated and then compared with the most used algorithms for optimization, such as PSO, WOA, and CS, with the aim of validating the method proposed herein, and determining the accuracy and speed of the IBAT algorithm. A policy for energy efficiency was then illustrated. The loss of power supply probability (LPSP) was then calculated to determine the degree of operating stability. As the IBAT is both easy to construct and does not require a high number of control parameters, it was determined to be more feasible. The modelled system was tested on a grid-connected PV system installed at the Libyan Center for Solar Energy Research and Studies in Tripoli, Libya. Annual data of irradiance, load profile, and temperature of the PV system were obtained and used for comparing the performances of the IBAT with the other algorithms. Obtained results prove that the proposed IBAT algorithm provides better optimal configuration than commonly used algorithms. The LPSP value of the IBAT algorithm is 0.0965 compared with 0.415, 0.625, and 0.845 for WOA, PSO, and CS, respectively.

Optimization-pdf

References

  1. D. J. Swider et al., “Conditions and costs for renewables electricity grid connection : Examples in Europe,” vol. 33, pp. 1832–1842, 2008, doi: 10.1016/j.renene.2007.11.005.
  2. J. Deb, Y. G. Yohanis, and B. Norton, “The impact of array inclination and orientation on the performance of a grid-connected photovoltaic system,” vol. 32, pp. 118–140, 2007, doi: 10.1016/j.renene.2006.05.006.
  3. A. W. Sawab, “Transactions on Energy Conversion,” vol. 11, no. 3, pp. 595–600, 1996.
  4. W. A. Omran, M. Kazerani, S. Member, and M. M. A. Salama, “Investigation of Methods for Reduction of Power Fluctuations Generated From Large Grid-Connected Photovoltaic Systems,” vol. 26, no. 1, pp. 318–327, 2011.
  5. N. Srisaen and A. Sangswang, “Effects of PV Grid-Connected System Location on a Distribution System,” vol. 00, pp. 852–855, 2006.
  6. Y. Sawle, S. C. Gupta, and A. K. Bohre, “Review of hybrid renewable energy systems with comparative analysis of o ff -grid hybrid system Loss of Power Supply Probability Loss of Load Probability,” Renew. Sustain. Energy Rev., vol. 81, no. June 2017, pp. 2217–2235, 2018, doi: 10.1016/j.rser.2017.06.033.
  7. J. Khoury, R. Mbayed, G. Salloum, and E. Monmasson, “Optimal sizing of a residential PV-battery backup for an intermittent primary energy source under realistic constraints,” Energy Build., vol. 105, pp. 206–216, 2015, doi: 10.1016/j.enbuild.2015.07.045.
  8. M. Paulitschke, “ScienceDirect ScienceDirect Comparison particle swarm and genetic based design The of on District algorithm Heating and Cooling algorithms for PV-hybrid systems with battery and hydrogen storage Assessing the feasibility of path using the heat demand-outd,” Energy Procedia, vol. 135, pp. 452–463, 2017, doi: 10.1016/j.egypro.2017.09.509.
  9. A. Chauhan and R. P. Saini, “Discrete harmony search based size optimization of Integrated Renewable Energy System for remote rural areas of Uttarakhand state in India,” Renew. Energy, vol. 94, pp. 587–604, 2016, doi: 10.1016/j.renene.2016.03.079.
  10. C. K. Shiva and V. Mukherjee, “A novel quasi-oppositional harmony search algorithm for automatic generation control of power system,” Appl. Soft Comput. J., vol. 35, pp. 749–765, 2015, doi: 10.1016/j.asoc.2015.05.054.
  11. A. Fetanat and E. Khorasaninejad, “Size optimization for hybrid photovoltaic – wind energy system using ant colony optimization for continuous domains based integer programming,” Appl. Soft Comput. J., vol. 31, pp. 196–209, 2015, doi: 10.1016/j.asoc.2015.02.047.
  12. O. Nadjemi, T. Nacer, A. Hamidat, and H. Salhi, “Optimal hybrid PV / wind energy system sizing : Application of cuckoo search algorithm for Algerian dairy farms,” Renew. Sustain. Energy Rev., vol. 70, no. December 2016, pp. 1352–1365, 2017, doi: 10.1016/j.rser.2016.12.038.
  13. C. K. Das, O. Bass, G. Kothapalli, T. S. Mahmoud, and D. Habibi, “Optimal placement of distributed energy storage systems in distribution networks using artificial bee colony algorithm,” Appl. Energy, vol. 232, no. July, pp. 212–228, 2018, doi: 10.1016/j.apenergy.2018.07.100.
  14. M. Tahani, N. Babayan, and A. Pouyaei, “Optimization of PV / Wind / Battery stand-alone system , using hybrid FPA / SA algorithm and CFD simulation , case study : Tehran,” Energy Convers. Manag., vol. 106, pp. 644–659, 2015, doi: 10.1016/j.enconman.2015.10.011.
  15. M. Lasheen and M. Abdel-salam, “Maximum power point tracking using Hill Climbing and ANFIS techniques for PV applications : A review and a novel hybrid approach,” Energy Convers. Manag., vol. 171, no. June, pp. 1002–1019, 2018, doi: 10.1016/j.enconman.2018.06.003.
  16. Z. Huang, Z. Xie, C. Zhang, S. Hwa, and Y. Xie, “Modeling and multi-objective optimization of a stand-alone PV-hydrogen- retired EV battery hybrid energy system,” vol. 181, no. August 2018, pp. 80–92, 2019, doi: 10.1016/j.enconman.2018.11.079.
  17. N. Ghorbani, A. Kasaeian, A. Toopshekan, and L. Bahrami, “Optimizing a hybrid wind-PV-battery system using GA-PSO and MOPSO for reducing cost and increasing reliability,” Energy, vol. 154, pp. 581–591, 2018, doi: 10.1016/j.energy.2017.12.057.
  18. M. A. M. Ramli, A. Hiendro, and Y. A. Al-turki, “Techno-economic energy analysis of wind / solar hybrid system : Case study for western coastal area of Saudi Arabia,” Renew. Energy, vol. 91, pp. 374–385, 2016, doi: 10.1016/j.renene.2016.01.071.
  19. A. Khiareddine, C. Ben, D. Rekioua, and M. Faouzi, “Sizing methodology for hybrid photovoltaic / wind / hydrogen / battery integrated to energy management strategy for pumping system,” Energy, vol. 153, pp. 743–762, 2018, doi: 10.1016/j.energy.2018.04.073.
  20. R. Belfkira, L. Zhang, and G. Barakat, “Optimal sizing study of hybrid wind / PV / diesel power generation unit,” Sol. Energy, vol. 85, no. 1, pp. 100–110, 2011, doi: 10.1016/j.solener.2010.10.018.
  21. C. Ghenai, T. Salameh, and A. Merabet, “ScienceDirect Technico-economic analysis of off grid solar PV / Fuel cell energy system for residential community in desert region,” Int. J. Hydrogen Energy, vol. 45, no. 20, pp. 11460–11470, 2018, doi: 10.1016/j.ijhydene.2018.05.110.
  22. A. Fathy, “A reliable methodology based on mine blast optimization algorithm for optimal sizing of hybrid PV-wind-FC system for remote area in Egypt,” Renew. Energy, vol. 95, pp. 367–380, 2016, doi: 10.1016/j.renene.2016.04.030.
  23. A. L. Bukar, C. W. Tan, and K. Y. Lau, “Optimal sizing of an autonomous photovoltaic/wind/battery/diesel generator microgrid using grasshopper optimization algorithm,” Sol. Energy, vol. 188, no. May, pp. 685–696, 2019, doi: 10.1016/j.solener.2019.06.050.
  24. T. Beck, H. Kondziella, G. Huard, and T. Bruckner, “Assessing the influence of the temporal resolution of electrical load and PV generation profiles on self-consumption and sizing of PV-battery systems,” Appl. Energy, vol. 173, pp. 331–342, 2016, doi: 10.1016/j.apenergy.2016.04.050.
  25. N. Marsa, L. Houcine, A. Zaafouri, and A. Chaari, “Optimal sizing of stand-alone hybrid photovoltaic / wind system using BAT algorithm,” Int. J. Ambient Energy, vol. 0, no. 0, pp. 1–9, 2019, doi: 10.1080/01430750.2019.1573756.
  26. J. Assaf and B. Shabani, “A novel hybrid renewable solar energy solution for continuous heat and power supply to standalone-alone applications with ultimate reliability and cost effectiveness,” Renew. Energy, vol. 138, pp. 509–520, 2019, doi: 10.1016/j.renene.2019.01.099.
  27. A. Yahiaoui, F. Fodhil, K. Benmansour, M. Tadjine, and N. Cheggaga, “Grey wolf optimizer for optimal design of hybrid renewable energy system PV-Diesel Generator-Battery : Application to the case of Djanet city of Algeria,” Sol. Energy, vol. 158, no. October, pp. 941–951, 2017, doi: 10.1016/j.solener.2017.10.040.
  28. A. Elbaz, A. P. Swarm, and O. Pso, “Using Crow Algorithm for Optimizing Size of Wind Power Plant / Hybrid PV in Libya,” no. 1, pp. 19–22, 2019.
  29. A. Laknizi, M. Bouya, A. Astito, and A. Ben, “Sizing a PV-Wind based hybrid system using deterministic approach,” Energy Convers. Manag., vol. 169, no. May, pp. 137–148, 2018, doi: 10.1016/j.enconman.2018.05.034.
  30. CSERS, “Centre for Solar Energy Research and Studies.” https://csers.ly/en/
  31. L. Xu, X. Ruan, C. Mao, B. Zhang, and Y. Luo, “An improved optimal sizing method for wind-solar-battery hybrid power system,” IEEE Trans. Sustain. Energy, vol. 4, no. 3, pp. 774–785, 2013, doi: 10.1109/TSTE.2012.2228509.
  32. F. Caballero, E. Sauma, and F. Yanine, “Business optimal design of a grid-connected hybrid PV ( photovoltaic ) - wind energy system without energy storage for an Easter Island ’ s block,” Energy, vol. 61, pp. 248–261, 2013, doi: 10.1016/j.energy.2013.08.030.
  33. M. Ali, Y. Jahromi, S. Farahat, and S. Masoud, “Civil Engineering and Environmental Systems Optimal size and cost analysis of stand-alone hybrid wind / photovoltaic power-generation systems,” vol. 6608, 2014, doi: 10.1080/10286608.2013.853752.
  34. M. Gómez, A. López, and F. Jurado, “Optimal placement and sizing from standpoint of the investor of Photovoltaics Grid-Connected Systems using Binary Particle Swarm Optimization,” Appl. Energy, vol. 87, no. 6, pp. 1911–1918, 2010, doi: 10.1016/j.apenergy.2009.12.021.
  35. E. Drury, P. Denholm, R. Margolis, E. Drury, P. Denholm, and R. Margolis, “The Impact of Different Economic Performance Metrics on the Perceived Value of Solar Photovoltaics The Impact of Different Economic Performance Metrics on the Perceived Value of Solar Photovoltaics,” no. October, 2011.