10.1007/s40095-019-00319-y

Designing of stand-alone hybrid PV/wind/battery system using improved crow search algorithm considering reliability index

HTML PDF
  • Sasan Moghaddam1,
  • Mehdi Bigdeli*1,
  • Majid Moradlou1,
  • Pierluigi Siano2,
  1. Department of Electrical Engineering, Zanjan Branch, Islamic Azad University, Zanjan, IR
  2. Department of Management and Innovation Systems, University of Salerno, Salerno, IT
Cover Image

Published in Issue 2019-09-09

How to Cite

Moghaddam, S., Bigdeli, M., Moradlou, M., & Siano, P. (2019). Designing of stand-alone hybrid PV/wind/battery system using improved crow search algorithm considering reliability index. International Journal of Energy and Environmental Engineering, 10(4 (December 2019). https://doi.org/10.1007/s40095-019-00319-y
Crossref
Scopus
Google Scholar
Europe PMC

HTML views: 33

PDF views: 143

Abstract

Abstract In this paper, the design of a hybrid renewable energy PV/wind/battery system is proposed for improving the load supply reliability over a study horizon considering the Net Present Cost (NPC) as the objective function to minimize. The NPC includes the costs related to the investment, replacement, operation, and maintenance of the hybrid system. The considered reliability index is the deficit power-hourly interruption probability of the load demand. The decision variables are the number of PV panels, wind turbines and batteries, capacity of transferred power by inverter, angle of PV panels, and wind tower height. To solve the optimization problem, a new algorithm named improved crow search algorithm (ICSA) is proposed. The design of the system is done for Zanjan city, Iran based on real data of solar radiation and wind speed of this area. The performance of the proposed ICSA is compared with crow search algorithm (CSA) and particle swarm optimization methods in different combinations of system. This comparison shows that the proposed ICSA algorithm has better performance than other methods.

Keywords

  • Hybrid PV/wind/battery system,
  • Net present cost,
  • Reliability index,
  • Improved crow search algorithm
HTML PDF

References

  1. Sawle et al. (2018) Socio-techno-economic design of hybrid renewable energy system using optimization techniques (pp. 459-472) https://doi.org/10.1016/j.renene.2017.11.058
  2. Nowdeh et al. (2019) Fuzzy multi-objective placement of renewable energy sources in distribution system with objective of loss reduction and reliability improvement using a novel hybrid method (pp. 761-779) https://doi.org/10.1016/j.asoc.2019.02.003
  3. Anoune et al. (2018) Sizing methods and optimization techniques for PV-wind based hybrid renewable energy system: a review (pp. 652-673) https://doi.org/10.1016/j.rser.2018.05.032
  4. Khan et al. (2018) Review of solar photovoltaic and wind hybrid energy systems for sizing strategies optimization techniques and cost analysis methodologies (pp. 937-947) https://doi.org/10.1016/j.rser.2018.04.107
  5. Rajabi-Ghahnavieh and Nowdeh (2014) Optimal PV–FC hybrid system operation considering reliability (pp. 325-333) https://doi.org/10.1016/j.ijepes.2014.03.043
  6. Bhandari et al. (2015) Optimization of hybrid renewable energy power systems: a review 2(1) (pp. 99-112) https://doi.org/10.1007/s40684-015-0013-z
  7. Ai et al. (2003) Computer-aided design of PV/wind hybrid system 28(10) (pp. 1491-1512) https://doi.org/10.1016/S0960-1481(03)00011-9
  8. Baghaee et al. (2016) Reliability/cost-based multi-objective Pareto optimal design of stand-alone wind/PV/FC generation microgrid system (pp. 1022-1041) https://doi.org/10.1016/j.energy.2016.09.007
  9. Gharavi et al. (2015) Imperial competitive algorithm optimization of fuzzy multi-objective design of a hybrid green power system with considerations for economics, reliability, and environmental emissions (pp. 427-437) https://doi.org/10.1016/j.renene.2015.01.029
  10. Moghaddam et al. (2019) Optimal sizing and energy management of stand-alone hybrid photovoltaic/wind system based on hydrogen storage considering LOEE and LOLE reliability indices using flower pollination algorithm (pp. 1412-1434) https://doi.org/10.1016/j.renene.2018.09.078
  11. Maleki et al. (2016) A novel framework for optimal design of hybrid renewable energy based autonomous energy systems: a case study for Namin, Iran (pp. 168-180) https://doi.org/10.1016/j.energy.2015.12.133
  12. Maleki and Askarzadeh (2014) Artificial bee swarm optimization for optimum sizing of a stand-alone PV/WT/BAT hybrid system considering LPSP concept (pp. 227-235) https://doi.org/10.1016/j.solener.2014.05.016
  13. Hongxing et al. (2008) Optimal sizing method for stand-alone hybrid solar–wind system with LPSP technology by using genetic algorithm (pp. 354-367) https://doi.org/10.1016/j.solener.2007.08.005
  14. Askarzadeh (2013) Developing a discrete harmony search algorithm for size optimization of wind–photovoltaic hybrid energy system (pp. 190-195) https://doi.org/10.1016/j.solener.2013.10.008
  15. Katsigiannis et al. (2010) Multi-objective genetic algorithm solution to the optimum economic and environmental performance problem of small autonomous hybrid power systems with renewable 4(5) (pp. 404-419) https://doi.org/10.1049/iet-rpg.2009.0076
  16. Yang et al. (2008) Optimal sizing method for stand-alone hybrid solar–wind system with LPSP technology by using genetic algorithm 82(4) (pp. 354-367) https://doi.org/10.1016/j.solener.2007.08.005
  17. Dufo and Bernal (2008) Multi-objective design of PV–wind– diesel–hydrogen–battery systems 33(12) (pp. 2559-2572) https://doi.org/10.1016/j.renene.2008.02.027
  18. Sharafi and ELMekkawy (2014) Multi-objective optimal design of hybrid renewable energy systems using PSO-simulation based approach (pp. 67-79) https://doi.org/10.1016/j.renene.2014.01.011
  19. Shi et al. (2015) Multi-objective optimal design of hybrid renewable energy systems using preference-inspired co-evolutionary approach (pp. 96-106) https://doi.org/10.1016/j.solener.2015.03.052
  20. Ahmadi and Abdi (2016) Application of the Hybrid Big Bang-Big Crunch algorithm for optimal sizing of a stand-alone hybrid PV/wind/battery system (pp. 366-374) https://doi.org/10.1016/j.solener.2016.05.019
  21. Maleki and Pourfayaz (2015) Optimal sizing of autonomous hybrid photovoltaic/wind/battery power system with LPSP technology by using evolutionary algorithms (pp. 471-483) https://doi.org/10.1016/j.solener.2015.03.004
  22. Hadidian-Moghaddam et al. (2016) Optimal sizing of a stand-alone hybrid photovoltaic/wind system using new grey wolf optimizer considering reliability 8(3) https://doi.org/10.1063/1.4950945
  23. Mohammedi K, Alem S (2013) Design and optimal energy management strategy for stand-alone PV–battery–Diesel systems using cuckoo search algorithm. In: IEEE 3rd international conference on systems and control, pp 230–235
  24. Bansal et al. (2013) Economic analysis and power management of a small autonomous hybrid power system (SAHPS) using biogeography based optimization (BBO) algorithm 4(1) (pp. 638-648) https://doi.org/10.1109/TSG.2012.2236112
  25. Jamadi et al. (2016) Very accurate parameter estimation of single and double diode solar cell models using a modified artificial bee colony algorithm 7(1) (pp. 13-25) https://doi.org/10.1007/s40095-015-0198-5
  26. Siddaiah and Saini (2016) A review on planning, configurations, modeling and optimization techniques of hybrid renewable energy systems for off grid applications (pp. 376-396) https://doi.org/10.1016/j.rser.2015.12.281
  27. Zanjan Climate Organization, Zanjan, Iran.
  28. http://www.zanjanmet.ir/
  29. Kaabeche et al. (2017) Firefly-inspired algorithm for optimal sizing of renewable hybrid system considering reliability criteria (pp. 727-738) https://doi.org/10.1016/j.solener.2017.06.070
  30. Askarzadeh (2016) A novel meta-heuristic method for solving constrained engineering optimization problems: crow search algorithm (pp. 1-12) https://doi.org/10.1016/j.compstruc.2016.03.001
  31. Aleem et al. (2017) Optimal resonance-free third-order high-pass filters based on minimization of the total cost of the filters using Crow Search Algorithm (pp. 381-394) https://doi.org/10.1016/j.epsr.2017.06.009
  32. Iranian Renewable Energy Organization (SUNA) Tehran, Iran.
  33. http://www.suna.org.ir/fa/sun/potential