Document Type : Original Article
Author
Georgia Institute of Technology, 801 Ferst Drive, Atlanta, GA 30332, USA
10.52293/SE.1.1.333348
Abstract
This paper presents an optimal size selection of a hybrid renewable PV/DG/Battery system in a remote area in Maowusu Desert, China. The idea is to select optimal numbers of PV panels, DGs, and battery storage units by minimizing of the total annual cost of the hybrid system. The optimization of the problem is performed based on a new improved version of Mayfly Algorithm (IMA) which is introduced for improving the effectiveness of the optimization in terms of accuracy, convergence, and consistency. Simulation results of the suggested algorithm are compared with some different optimization algorithm to show the prominence of the method. The proposed method shows that the optimal numbers for the optimized system includes 28 PV panels, 88 battery units, and 1 DG unit. Final results show that utilizing the suggested hybrid system gives the best minimum operational cost of the system compared with others.
Keywords
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29. Borhanazad, H., et al., Optimization of micro-grid system using MOPSO. Renewable Energy, 2014. 71: p. 295-306.
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31. Borowy, B.S. and Z.M. Salameh, Methodology for optimally sizing the combination of a battery bank and PV array in a wind/PV hybrid system. IEEE Transactions on energy conversion, 1996. 11(2): p. 367-375.
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37. Pan, W.-T., A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowledge-Based Systems, 2012. 26: p. 69-74.
38. Suryoatmojo, H., et al., Optimal design of wind-PV-diesel-battery system using genetic algorithm. IEEJ Transactions on Power and Energy, 2009. 129(3): p. 413-420.
39. Yang, H., et al., Optimal sizing method for stand-alone hybrid solar–wind system with LPSP technology by using genetic algorithm. Solar energy, 2008. 82(4): p. 354-367.
40. Dufo-Lopez, R. and J.L. Bernal-Agustín, Multi-objective design of PV–wind–diesel–hydrogen–battery systems. Renewable energy, 2008. 33(12): p. 2559-2572.
41. Yahiaoui, A., et al., Grey wolf optimizer for optimal design of hybrid renewable energy system PV-Diesel Generator-Battery: Application to the case of Djanet city of Algeria. Solar Energy, 2017. 158: p. 941-951.
2. Cao, Y., et al., Multi-objective optimization of a PEMFC based CCHP system by meta-heuristics. Energy Reports, 2019. 5: p. 1551-1559.
3. Cao, Y., et al., Experimental modeling of PEM fuel cells using a new improved seagull optimization algorithm. Energy Reports, 2019. 5: p. 1616-1625.
4. Alizadeh, E., et al., Investigation of contact pressure distribution over the active area of PEM fuel cell stack. International Journal of Hydrogen Energy, 2016. 41(4): p. 3062-3071.
5. Gollou, A.R. and N. Ghadimi, A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets. Journal of Intelligent & Fuzzy Systems, 2017. 32(6): p. 4031-4045.
6. do Nascimento, D.A., et al., Sustainable adoption of connected vehicles in the Brazilian landscape: policies, technical specifications and challenges. Transactions on Environment and Electrical Engineering, 2019. 3(1): p. 44-62.
7. Hosseini, H., et al., A novel method using imperialist competitive algorithm (ICA) for controlling pitch angle in hybrid wind and PV array energy production system. International Journal on Technical and Physical Problems of Engineering (IJTPE), 2012. 11: p. 145-152.
8. Hamian, M., et al., A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on mixed integer genetic algorithm. Engineering Applications of Artificial Intelligence, 2018. 72: p. 203-212.
9. Hosseini Firouz, M. and N. Ghadimi, Optimal preventive maintenance policy for electric power distribution systems based on the fuzzy AHP methods. Complexity, 2016. 21(6): p. 70-88.
10. Leng, H., et al., A new wind power prediction method based on ridgelet transforms, hybrid feature selection and closed-loop forecasting. Advanced Engineering Informatics, 2018. 36: p. 20-30.
11. Fan, X., et al., Multi-objective optimization for the proper selection of the best heat pump technology in a fuel cell-heat pump micro-CHP system. Energy Reports, 2020. 6: p. 325-335.
12. 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.
13. Yu, D., et al., System identification of PEM fuel cells using an improved Elman neural network and a new hybrid optimization algorithm. Energy Reports, 2019. 5: p. 1365-1374.
14. Liu, Y., W. Wang, and N. Ghadimi, Electricity load forecasting by an improved forecast engine for building level consumers. Energy, 2017. 139: p. 18-30.
15. Mirzapour, F., et al., A new prediction model of battery and wind-solar output in hybrid power system. Journal of Ambient Intelligence and Humanized Computing, 2019. 10(1): p. 77-87.
16. 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.
17. 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.
18. 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.
19. Yuan, Z., et al., A new technique for optimal estimation of the circuit-based PEMFCs using developed Sunflower Optimization Algorithm. Energy Reports, 2020. 6: p. 662-671.
20. 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.
21. Nejad, H.C., et al., Reliability based optimal allocation of distributed generations in transmission systems under demand response program. Electric Power Systems Research, 2019. 176: p. 105952.
22. Razmjooy, N., F.R. Sheykhahmad, and N. Ghadimi, A hybrid neural network–world cup optimization algorithm for melanoma detection. Open Medicine, 2018. 13(1): p. 9-16.
23. Shamel, A. and N. Ghadimi, Hybrid PSOTVAC/BFA technique for tuning of robust PID controller of fuel cell voltage. 2016.
24. Das, B.K. and F. Zaman, Performance analysis of a PV/Diesel hybrid system for a remote area in Bangladesh: Effects of dispatch strategies, batteries, and generator selection. Energy, 2019. 169: p. 263-276.
25. Bukar, A.L., C.W. Tan, and K.Y. Lau, Optimal sizing of an autonomous photovoltaic/wind/battery/diesel generator microgrid using grasshopper optimization algorithm. Solar Energy, 2019. 188: p. 685-696.
26. Makhdoomi, S. and A. Askarzadeh, Optimizing operation of a photovoltaic/diesel generator hybrid energy system with pumped hydro storage by a modified crow search algorithm. Journal of Energy Storage, 2020. 27: p. 101040.
27. Ghaffari, A. and A. Askarzadeh, Design optimization of a hybrid system subject to reliability level and renewable energy penetration. Energy, 2020. 193: p. 116754.
28. Suryoatmojo, H., A.A. Elbaset, and T. Hiyama, Economic and reliability evaluation of wind-Diesel-Battery system for isolated island considering CO2 emission. IEEJ Transactions on Power and Energy, 2009. 129(8): p. 1000-1008.
29. Borhanazad, H., et al., Optimization of micro-grid system using MOPSO. Renewable Energy, 2014. 71: p. 295-306.
30. Suryoatmojo, H., et al., Optimal Sizing and Control Strategy of Hybrid PV-Diesel-Battery Systems for Isolated Island*. no, 2014. 1: p. 1-6.
31. Borowy, B.S. and Z.M. Salameh, Methodology for optimally sizing the combination of a battery bank and PV array in a wind/PV hybrid system. IEEE Transactions on energy conversion, 1996. 11(2): p. 367-375.
32. Brittain, J.E. and M. Sartori, Ephemeroptera:(Mayflies), in Encyclopedia of insects. 2009, Elsevier. p. 328-334.
33. Zervoudakis, K. and S. Tsafarakis, A mayfly optimization algorithm. Computers & Industrial Engineering, 2020: p. 106559.
34. Salgotra, R. and U. Singh, Application of mutation operators to flower pollination algorithm. Expert Systems with Applications, 2017. 79: p. 112-129.
35. Bozorgi, S.M. and S. Yazdani, IWOA: An improved whale optimization algorithm for optimization problems. Journal of Computational Design and Engineering, 2019. 6(3): p. 243-259.
36. Heidari, A.A., et al., Harris hawks optimization: Algorithm and applications. Future generation computer systems, 2019. 97: p. 849-872.
37. Pan, W.-T., A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowledge-Based Systems, 2012. 26: p. 69-74.
38. Suryoatmojo, H., et al., Optimal design of wind-PV-diesel-battery system using genetic algorithm. IEEJ Transactions on Power and Energy, 2009. 129(3): p. 413-420.
39. Yang, H., et al., Optimal sizing method for stand-alone hybrid solar–wind system with LPSP technology by using genetic algorithm. Solar energy, 2008. 82(4): p. 354-367.
40. Dufo-Lopez, R. and J.L. Bernal-Agustín, Multi-objective design of PV–wind–diesel–hydrogen–battery systems. Renewable energy, 2008. 33(12): p. 2559-2572.
41. Yahiaoui, A., et al., Grey wolf optimizer for optimal design of hybrid renewable energy system PV-Diesel Generator-Battery: Application to the case of Djanet city of Algeria. Solar Energy, 2017. 158: p. 941-951.
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