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A Comprehensive Review of Nature-Inspired Search Techniques Used in Estimating Optimal Configuration Size, Cost, and Reliability of a Mini-grid HRES: A Systemic Review

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Computational Science and Its Applications – ICCSA 2021 (ICCSA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12957))

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Abstract

Nature-inspired algorithms use random exploration and exploitation tactics as a searching strategy to explore a search space. These two searching schemes are harmonized in nature-inspired search techniques to solve any optimization problem. Although several traditional approaches have been applied to the design of optimal HRES, there is still a challenge of finding a near-optimal approach to estimate the configuration size, cost, and reliability of mini-grid HRES. In this paper, we reviewed the state-of-the-art optimization approaches that have been applied in estimating the configuration size, cost, and reliability of mini-grid HRES. A desktop-based research method was adopted in which a total of 49 scholarly articles which tie well to the topic was selected for a thorough review. Various nature-inspired search methods proposed and/or applied in the last 5 years (2016–2021) by different researchers in solving the optimization problem of HRES were showcased in this paper. The review suggested that the optimal design of HRES in most cases seeks to minimize a cost function and maximizes the reliability of the system to meet the load requirement. Again, based on the diverse scenarios and increasing complexities of HRES, nature-inspired algorithms promise better near-optimal solutions than their competitors. Furthermore, the review suggested that nature-inspired search techniques have been applied extensively in HRES optimization. Moreover, several studies have also hybridized two or more algorithms to improve the searching strategies for better performance of HRES. These findings among others suggest opportunities for future research in the design of near-optimal HRES. The review holds salient implications for researchers and industry professionals. It elucidates the chances to design a reliable, cost-efficient, and effective mini-grid HRES yet have economic benefits to the users.

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References

  1. Committee on Climate Change: Net Zero Technical Report (2019). https://www.theccc.org.uk/publication/net-zero-technical-report/

  2. Quarton, C.J., et al.: The curious case of the conflicting roles of hydrogen in global energy scenarios. Sustain. Energy Fuels 4(80), 80–95 (2020). https://doi.org/10.1039/c9se00833k

    Article  Google Scholar 

  3. Frank, C., Fiedler, S., Crewell, S.: Balancing potential of natural variability and extremes in photovoltaic and wind energy production for European countries Christopher. Renew. Energy (2020). https://doi.org/10.1016/j.renene.2020.07.103

  4. Mugodo, K., Magama, P.P., Dhavu, K.: Biogas production potential from agricultural and agro-processing waste in South Africa. Waste Biomass Valor. 8(7), 2383–2392 (2017). https://doi.org/10.1007/s12649-017-9923-z

    Article  Google Scholar 

  5. Votteler, R., Brent, A.: A literature review on the potential of renewable electricity sources for mining operations in South Africa. J. Energy Southern Africa 27(2), 1–21 (2016)

    Article  Google Scholar 

  6. Adesanya, A., Misra, S., Maskeliunas, R., Damasevicius, R.: Prospects of ocean-based renewable energy for West Africa’s sustainable energy future. Smart Sustain. Built Environ. 10(1), 37–50 (2021). https://doi.org/10.1108/SASBE-05-2019-0066

    Article  Google Scholar 

  7. Ghenai, C., Salameh, T., Merabet, A.: Technico-economic analysis of off grid solar PV/Fuel cell energy system for residential community in desert region. Int. J. Hydrogen Energy 45(20), 11460–11470 (2020). https://doi.org/10.1016/j.ijhydene.2018.05.110

    Article  Google Scholar 

  8. Singh, S., Singh, M., Kaushik, S.C.: Feasibility study of an islanded microgrid in rural area consisting of PV, wind, biomass and battery energy storage system. Energy Convers. Manag. 128, 178–190 (2016). https://doi.org/10.1016/j.enconman.2016.09.046

    Article  Google Scholar 

  9. Zafar, A., Shafique, A., Nazir, Z., Zia, M.F.: A comparison of optimization techniques for energy scheduling of hybrid power generation system. In: Proceedings 21st International Multi Topic Conference INMIC 2018, no. June 2019, pp. 1–6 (2018). https://doi.org/10.1109/INMIC.2018.8595665

  10. Khezri, R., Mahmoudi, A.: Review on the state-of-the-art multi-objective optimisation of hybrid standalone/gridconnected energy systems. IET Gener. Transm. Distrib. 14(20), 4285–4300 (2020). https://doi.org/10.1049/iet-gtd.2020.0453

    Article  Google Scholar 

  11. Hou, R., Yang, Y., Yuan, Q., Chen, Y.: Research and application of hybrid wind-energy forecasting models based on cuckoo search optimization. pp. 1–18 (2019)

    Google Scholar 

  12. Lu, J., Wang, W., Zhang, Y., Cheng, S.: Hybrid energy system using entropy weight method (2017). https://doi.org/10.3390/en10101664

  13. Donado, K., Navarro, L., Christian, G., Quintero, M., Pardo, M.: HYRES: a multi-objective optimization tool for proper configuration of renewable hybrid energy systems. Energies 13(1), 26 (2019). https://doi.org/10.3390/en13010026

    Article  Google Scholar 

  14. Yong, Z., Shaowu, L.: Economic evaluation and configuration optimization strategy of hybrid renewable energy generation system: a review. In: Proceedings 32nd Chinese Control and Decision Conference (CCDC) 2020, pp. 729–734 (2020). https://doi.org/10.1109/CCDC49329.2020.9164560

  15. Torres-madroñero, J.L., Nieto-londoño, C.: Hybrid energy systems sizing for the colombian context : a genetic algorithm and particle swarm, pp. 1–30 (2020). https://doi.org/10.3390/en13215648

  16. Urbanucci, L., Ettorre, F.D., Testi, D.: A comprehensive methodology for the integrated optimal sizing and operation of cogeneration systems with thermal energy storage (2019). https://doi.org/10.3390/en12050875

  17. Nguyen, T., Nguyen, L.V., Jung, J.J., Agbehadji, I.E.: Bio-inspired approaches for smart energy management : state of the art and challenges, pp. 1–24 (2020). https://doi.org/10.3390/su12208495

  18. Khan, A., et al.: Enhanced evolutionary sizing algorithms for optimal sizing of a stand-alone PV-WT-battery hybrid system. Appl. Sci. 9(23), 5197 (2019). https://doi.org/10.3390/app9235197

    Article  Google Scholar 

  19. Frimpong, S.O.: Nature-inspired search method for cost optimization of hybrid renewable energy generation at the edge (2020)

    Google Scholar 

  20. Alzahrani, A., Zohdy, M., Yan, B.: An overview of optimization approaches for operation of hybrid distributed energy systems with photovoltaic and diesel turbine generator. Electric Power Syst. Res. 191, 106877 (2021). https://doi.org/10.1016/j.epsr.2020.106877

    Article  Google Scholar 

  21. Diab, A.A.Z., Sultan, H.M., Kuznetsov, O.N.: Optimal sizing of hybrid solar/wind/hydroelectric pumped storage energy system in Egypt based on different meta-heuristic techniques. Environ. Sci. Pollut. Res. 27(26), 32318–32340 (2019). https://doi.org/10.1007/s11356-019-06566-0

    Article  Google Scholar 

  22. Das, B., Hassan, R., Mohammad Shahed, H.K., Tushar, F., Hasan, M., Das, P.: Techno-economic and environmental assessment of a hybrid renewable energy system using multi-objective genetic algorithm: a case study for remote Island in Bangladesh. Energy Convers. Manag. 230, 113823 (2021). https://doi.org/10.1016/j.enconman.2020.113823

    Article  Google Scholar 

  23. Clarke, D.P., Al-Abdeli, Y.M., Kothapalli, G.: Multi-objective optimisation of renewable hybrid energy systems with desalination. Energy 88, 457–468 (2015). https://doi.org/10.1016/j.energy.2015.05.065

    Article  Google Scholar 

  24. Barakat, S., Ibrahim, H., Elbaset, A.A.: Multi-objective optimization of grid-connected pv-wind hybrid system considering reliability, cost, and environmental aspects. Sustain. Cities Soc. 60, 102178 (2020). https://doi.org/10.1016/j.scs.2020.102178

    Article  Google Scholar 

  25. Micangeli, A., Duenas-Martinez, P.: Optimal design of isolated mini-grids with deterministic methods: matching predictive operating strategies with low computational requirements. Energies 13(16), 4214 (2020). https://doi.org/10.3390/en13164214

    Article  Google Scholar 

  26. Khan, F.A., Pal, N., Saeed, S.H.: Review of solar photovoltaic and wind hybrid energy systems for sizing strategies optimization techniques and cost analysis methodologies. Renew. Sustain. Energy Rev. 92(March), 937–947 (2018). https://doi.org/10.1016/j.rser.2018.04.107

    Article  Google Scholar 

  27. Sinha, S., Chandel, S.S.: Review of recent trends in optimization techniques for solar photovoltaic – wind based hybrid energy systems. Renew. Sustain. Energy Rev. 50, 755–769 (2015). https://doi.org/10.1016/j.rser.2015.05.040

    Article  Google Scholar 

  28. Eriksson, E.L.V., Gray, E.M.: Optimization and integration of hybrid renewable energy hydrogen fuel cell energy systems – a critical review. Appl. Energy 202, 348–364 (2017). https://doi.org/10.1016/j.apenergy.2017.03.132

    Article  Google Scholar 

  29. Husain, S., Shrivastava, N.A.: A comparative analysis of multi-objective optimization algorithms for stand-alone hybrid renewable energy system. In: ICIMIA, pp. 255–260 (2020)

    Google Scholar 

  30. Mohseni, S., Brent, A.C., Burmester, D.: A comparison of metaheuristics for the optimal capacity planning of an isolated, battery-less, hydrogen-based micro-grid. Appl. Energy 259, 114224 (2020). https://doi.org/10.1016/j.apenergy.2019.114224

    Article  Google Scholar 

  31. Geleta, D.K., Manshahia, M.S.: A hybrid of grey wolf optimization and genetic algorithm for optimization of hybrid wind and solar renewable energy system. J. Oper. Res. Soc. China (2021). https://doi.org/10.1007/s40305-021-00341-0

  32. Ebrahimi, A., Attar, S., Farhang-Moghaddam, B.: A multi-objective decision model for residential building energy optimization based on hybrid renewable energy systems. Int. J. Green Energy, 1–18 (2021). https://doi.org/10.1080/15435075.2021.1880911

  33. Sultan, H.M., Menesy, A.S., Kamel, S., Korashy, A., Almohaimeed, S.A., Abdel-Akher, M.: An improved artificial ecosystem optimization algorithm for optimal configuration of a hybrid PV/WT/FC energy system. Alexandria Eng. J. 60(1), 1001–1025 (2021). https://doi.org/10.1016/j.aej.2020.10.027

    Article  Google Scholar 

  34. Ghaffari, A., Askarzadeh, A.: Design optimization of a hybrid system subject to reliability level and renewable energy penetration. Energy 193, 116754 (2020). https://doi.org/10.1016/j.energy.2019.116754

    Article  Google Scholar 

  35. Mokhtara, C., Negrou, B., Settou, N., Settou, B., Samy, M.M.: Design optimization of off-grid hybrid renewable energy systems considering the effects of building energy performance and climate change: case study of Algeria. Energy 219, 119605 (2021). https://doi.org/10.1016/j.energy.2020.119605

    Article  Google Scholar 

  36. Ashraf, M.A., Liu, Z., Alizadeh, A., Nojavan, S., Jermsittiparsert, K., Zhang, D.: Designing an optimized configuration for a hybrid PV/Diesel/battery energy system based on metaheuristics: a case study on Gobi desert. J. Clean. Prod. 270, 122467 (2020). https://doi.org/10.1016/j.jclepro.2020.122467

    Article  Google Scholar 

  37. Kharrich, M., Kamel, S., Abdeen, M., Mohammed, O.H., Akherraz, M.: Developed approach based on equilibrium optimizer for optimal design of hybrid PV/Wind/Diesel/Battery Microgrid in Dakhla, Morocco, pp. 13655–13670 (2021). https://doi.org/10.1109/ACCESS.2021.3051573

  38. Ganguly, P., Kalam, A., Zayegh, A.: Solar–wind hybrid renewable energy system: current status of research on configurations, control, and sizing methodologies. In: Hybrid-Renewable Energy Systems in Microgrids, Elsevier Ltd., pp. 219–248 (2018)

    Google Scholar 

  39. Twaha, S., Ramli, M.A.M.: A review of optimization approaches for hybrid distributed energy generation systems: off-grid and grid-connected systems. Sustain. Cities Soc. 41(April), 320–331 (2018). https://doi.org/10.1016/j.scs.2018.05.027

    Article  Google Scholar 

  40. Theo, W.L., Lim, J.S., Ho, W.S., Hashim, H., Lee, C.T.: Review of distributed generation (DG) system planning and optimisation techniques: comparison of numerical and mathematical modelling methods. Renew. Sustain. Energy Rev. 67, 531–573 (2017). https://doi.org/10.1016/j.rser.2016.09.063

    Article  Google Scholar 

  41. Zahraee, S.M., Khalaji Assadi, M., Saidur, R.: Application of artificial intelligence methods for hybrid energy system optimization. Renew. Sustain. Energy Rev. 66, 617–630 (2016). https://doi.org/10.1016/j.rser.2016.08.028

    Article  Google Scholar 

  42. Mohamed, M.A., Eltamaly, A.M., Alolah, A.I.: Swarm intelligence-based optimization of grid-dependent hybrid renewable energy systems. Renew. Sustain. Energy Rev. 77, 515–524 (2017). https://doi.org/10.1016/j.rser.2017.04.048

    Article  Google Scholar 

  43. Tezer, T., Yaman, R., Yaman, Gül.şen: Evaluation of approaches used for optimization of stand-alone hybrid renewable energy systems. Renew. Sustain. Energy Rev. 73, 840–853 (2017). https://doi.org/10.1016/j.rser.2017.01.118

    Article  Google Scholar 

  44. Xiao, H., Pei, W., Dong, Z., Kong, L., Wang, D.: Application and comparison of metaheuristic and new metamodel based global optimization. https://doi.org/10.3390/en11010085

  45. Mandal, S.: Modeling of photovoltaic systems using modified elephant swarm water search algorithm. Int. J. Modell. Simul. 40(6), 436–455 (2020). https://doi.org/10.1080/02286203.2019.1650488

    Article  Google Scholar 

  46. El-salam, M., Beshr, E., Eteiba, M.: A new hybrid technique for minimizing power losses in a distribution system by optimal sizing and siting of distributed generators with network reconfiguration. Energies 11(12), 3351 (2018). https://doi.org/10.3390/en11123351

    Article  Google Scholar 

  47. Aala Kalananda, V.K.R., Komanapalli, V.L.N.: Nature-inspired optimization algorithms for renewable energy generation, distribution and management—a comprehensive review. In: Vinoth Kumar, B., Sivakumar, P., Rajan Singaravel, M. M., Vijayakumar, K. (eds.) Intelligent Paradigms for Smart Grid and Renewable Energy Systems. AIS, pp. 139–226. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-9968-2_6

    Chapter  Google Scholar 

  48. Nazari-heris, M., Mohammadi-ivatloo, B., Asadi, S., Kim, H., Geem, Z.W.: Harmony search algorithm for energy system applications: an updated review and analysis. J. Exp. Theor. Artif. Intell. 31(5), 723–749 (2019). https://doi.org/10.1080/0952813X.2018.1550814

    Article  Google Scholar 

  49. Ashraf, M.M., Malik, T.N.: A hybrid teaching–learning-based optimizer with novel radix-5 mapping procedure for minimum cost power generation planning considering renewable energy sources and reducing emission. Electr. Eng. 102(4), 2567–2582 (2020). https://doi.org/10.1007/s00202-020-01044-0

    Article  Google Scholar 

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Authors and Affiliations

  1. ICT and Society Research Group, Department of Information Technology, Durban University of Technology, Durban, South Africa

    Samuel Ofori Frimpong, Richard C. Millham & Israel Edem Agbehadji

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  1. Samuel Ofori Frimpong
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  2. Richard C. Millham
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  3. Israel Edem Agbehadji
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Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Monash University, Clayton, VIC, Australia

    David Taniar

  7. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

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Frimpong, S.O., Millham, R.C., Agbehadji, I.E. (2021). A Comprehensive Review of Nature-Inspired Search Techniques Used in Estimating Optimal Configuration Size, Cost, and Reliability of a Mini-grid HRES: A Systemic Review. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12957. Springer, Cham. https://doi.org/10.1007/978-3-030-87013-3_37

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  • DOI: https://doi.org/10.1007/978-3-030-87013-3_37

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