Document Type : Original Article


Science Explorer Group, London, United Kingdom



Renewable energy technology is quickly developing in last decades due to the increasing attention of countries to sustainable and clean energy, and is constantly evolving in terms of technology. Nevertheless, there are obstacles to this case, such as rising costs and declining reliability due to the volatility of renewable power resources. Renewable technologies with the aim of using one source to cover other weaknesses, is one way to overcome these obstacles. In the present paper, a technique for optimum sizing of the components in a hybrid renewable power system (HRPS) consists of PV panels, electrolyzer, fuel cell, wind turbines, and the converters has been studied with keeping the value of the Net Present Cost (NPC) minimum. For giving a more efficient power generation cost, the optimization is obtained by using an Adaptive version of Wildebeest Herd Optimizer (AWHO). The main profit of the suggested algorithm is to resolve the main disadvantages of the other metaheuristic from the literature, like reliability, convergence speed, premature convergence, and accuracy, as it is possible. The proposed system is performed to a real-world case study in Yantai, China. Simulation results are put in comparison with several latest methods.


1.    Lian, J., et al., A review on recent sizing methodologies of hybrid renewable energy systems. Energy Conversion and Management, 2019. 199: p. 112027.
2.    Khan, M.J., Review of Recent Trends in Optimization Techniques for Hybrid Renewable Energy System. Archives of Computational Methods in Engineering, 2020: p. 1-11.
3.    Muh, E. and F. Tabet, Comparative analysis of hybrid renewable energy systems for off-grid applications in Southern Cameroons. Renewable energy, 2019. 135: p. 41-54.
4.    Sadeghi, D., A.H. Naghshbandy, and S. Bahramara, Optimal sizing of hybrid renewable energy systems in presence of electric vehicles using multi-objective particle swarm optimization. Energy, 2020. 209: p. 118471.
5.    Aljohani, T.M., A.F. Ebrahim, and O. Mohammed, Hybrid Microgrid Energy Management and Control Based on Metaheuristic-Driven Vector-Decoupled Algorithm Considering Intermittent Renewable Sources and Electric Vehicles Charging Lot. Energies, 2020. 13(13): p. 3423.
6.    Wu, L., et al., Hybrid extreme learning machine with meta-heuristic algorithms for monthly pan evaporation prediction. Computers and Electronics in Agriculture, 2020. 168: p. 105115.
7.    Othman, A.M., M.h. Helaimi, and H.A. Gabbar, Enhanced Nature-Inspired Meta-Heuristic Algorithm for Microgrid Performance Improvement. Electric Power Components and Systems, 2020: p. 1-12.
8.    Fei, X., R. Xuejun, and N. Razmjooy, Optimal configuration and energy management for combined solar chimney, solid oxide electrolysis, and fuel cell: a case study in Iran. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2019: p. 1-21.
9.    Fathabadi, H., Novel high efficient speed sensorless controller for maximum power extraction from wind energy conversion systems. Energy Conversion and Management, 2016. 123: p. 392-401.
10.    Jackson, P., On the displacement height in the logarithmic velocity profile. Journal of fluid mechanics, 1981. 111: p. 15-25.
11.    Gong, W. and N. razmjooy, A new optimisation algorithm based on OCM and PCM solution through energy reserve. International Journal of Ambient Energy, 2020: p. 1-14.
12.    Fathabadi, H., Novel high efficiency DC/DC boost converter for using in photovoltaic systems. Solar Energy, 2016. 125: p. 22-31.
13.    Gomez-Merchan, R., et al., Binary Search-Based Flexible Power Point Tracking Algorithm for Photovoltaic Systems. IEEE Transactions on Industrial Electronics, 2020.
14.    Ott, S., et al., Ionomer distribution control in porous carbon-supported catalyst layers for high-power and low Pt-loaded proton exchange membrane fuel cells. Nature Materials, 2020. 19(1): p. 77-85.
15.    Guo, Y., et al., An optimal configuration for a battery and PEM fuel cell-based hybrid energy system using developed Krill herd optimization algorithm for locomotive application. Energy Reports, 2020. 6: p. 885-894.
16.    Zhang, G., et al., Optimal location and size of a grid-independent solar/hydrogen system for rural areas using an efficient heuristic approach. Renewable Energy, 2020.
17.    Zhang, G., C. Xiao, and N. Razmjooy, Optimal Parameter Extraction of PEM Fuel Cells by Meta-heuristics. International Journal of Ambient Energy, 2020(just-accepted): p. 1-22.
18.    Li, D., et al., Maximum power efficiency operation and generalized predictive control of PEM (proton exchange membrane) fuel cell. Energy, 2014. 68: p. 210-217.
19.    Sengupta, S., S. Basak, and R.A. Peters, Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives. Machine Learning and Knowledge Extraction, 2019. 1(1): p. 157-191.
20.    Mir, M., et al., Employing a Gaussian Particle Swarm Optimization method for tuning Multi Input Multi Output‐fuzzy system as an integrated controller of a micro‐grid with stability analysis. Computational Intelligence, 2020. 36(1): p. 225-258.
21.    Shamel, A. and N. Ghadimi, Hybrid PSOTVAC/BFA technique for tuning of robust PID controller of fuel cell voltage. 2016.
22.    Razmjooy, N., M. Khalilpour, and M. Ramezani, A new meta-heuristic optimization algorithm inspired by FIFA world cup competitions: theory and its application in PID designing for AVR system. Journal of Control, Automation and Electrical Systems, 2016. 27(4): p. 419-440.
23.    Razmjooy, N., V.V. Estrela, and H.J. Loschi, Entropy-Based Breast Cancer Detection in Digital Mammograms Using World Cup Optimization Algorithm. International Journal of Swarm Intelligence Research (IJSIR), 2020. 11(3): p. 1-18.
24.    Dhiman, G. and V. Kumar, Emperor penguin optimizer: A bio-inspired algorithm for engineering problems. Knowledge-Based Systems, 2018. 159: p. 20-50.
25.    Baliarsingh, S.K., et al., Analysis of high-dimensional biomedical data using an evolutionary multi-objective emperor penguin optimizer. Swarm and Evolutionary Computation, 2019. 48: p. 262-273.
26.    Tang, F., J. Li, and N. Zafetti, Optimization of residential building envelopes using an improved Emperor Penguin Optimizer. Engineering with Computers, 2020: p. 1-13.
27.    Arora, S. and S. Singh, Butterfly optimization algorithm: a novel approach for global optimization. Soft Computing, 2019. 23(3): p. 715-734.
28.    Arora, S. and S. Singh, An improved butterfly optimization algorithm with chaos. Journal of Intelligent & Fuzzy Systems, 2017. 32(1): p. 1079-1088.
29.    Xiang, W.-l., et al., A dynamic shuffled differential evolution algorithm for data clustering. Neurocomputing, 2015. 158: p. 144-154.
30.    Liang, J.J., B.Y. Qu, and P.N. Suganthan, Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore, 2013. 635.
31.    Wang, P.C. and T.E. Shoup, A poly-hybrid PSO optimization method with intelligent parameter adjustment. Advances in Engineering Software, 2011. 42(8): p. 555-565.
32.    Rashedi, E., H. Nezamabadi-Pour, and S. Saryazdi, GSA: a gravitational search algorithm. Information sciences, 2009. 179(13): p. 2232-2248.
33.    Dhiman, G. and V. Kumar, Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Advances in Engineering Software, 2017. 114: p. 48-70.
34.    Amali, D. and M. Dinakaran, Wildebeest herd optimization: A new global optimization algorithm inspired by wildebeest herding behaviour. Journal of Intelligent & Fuzzy Systems, 2019(Preprint): p. 1-14.
35.    HOMER, P., NASA surface meteorology and solar energy database. 2019.