• Bankole Samson Sesan
  • Isiyaku Abubakar
  • Nasiru B. Kadandani
  • Isaac B. Olalekan
Keywords: Photovoltaic (PV), wind turbine (WT), battery energy storage (BES), improved grey wolf optimization algorithm (IGWOA), loss of power supply probability (LPSP)


This paper presents an improved grey wolf optimization algorithm (IGWOA) for optimal sizing of an isolated photovoltaic (PV), wind turbine (WT), and battery energy storage (BES) hybrid microgrid. To demonstrate the effectiveness of the proposed approach, atmospheric data sets comprising of wind, solar, and temperature of Kaduna International Airport were collected from Nigerian Meteorological Agency while the load demand data was collected from Kaduna International Airport Electricity Distribution Center. The microgrid optimal sizing was formulated as a constrained single objective optimization problem. Constraints including, loss of power supply probability (LPSP), power balance, generation limits and battery state of charge (SOC) were imposed. Three simulation scenarios were considered. Firstly, the target allowable maximum LPSP was fixed at 25% and the algorithm was able to determine the optimal sizing of the hybrid microgrid components and minimize the initial cost from 169,880.00 USD to 112,356.40 USD per annum resulting in 34% savings in cost. Secondly, the effect of the target allowable maximum LPSP variation was investigated, and it was found that the total installed capacity of the system decreases with increase in LPSP thereby decreasing the total cost. Additionally, a novel electricity price index (EPI) was introduced in order to quantify the degree of optimality of the solution. The EPI was found to increase exponentially with increase in LPSP, resulting in an EPI of < 0.05USD/kWh at 20% LPSP. Lastly, to validate the proposed approach, a comparative analysis between the IGWOA and other algorithms was carried out, and the proposed IGWOA proved applicable.


Administration, U. E. (2016). International Energy Outlook. Washington, DC, USA: U.S. Energy Information Administration.

Ainah, P. E. (2015). Development of Microgrid in Sub-Saharan Africa: An overview. International Review of Electrical Engineering, Vol. 10, No. 5, pp. 663-645, doi: https://doi.org/10.15866/iree.v10i5.5943 DOI: https://doi.org/10.15866/iree.v10i5.5943

Akinyele, D. E. (2016). Stategy for Developing Energy Systems for Remote Communities: Insights to Best Practices and Sustability. Sustainable Energy Technologies and Assessment, Vol 16, pp. 106-127, https://doi.org/10.1016/j.seta.2016.05.001 DOI: https://doi.org/10.1016/j.seta.2016.05.001

Akram, U., K. Muhammad, & Shafiq, S. (2017). Optimal Sizing of a Wind/Solar/Battery Hybrid Grid-Connected Microgrid System. IET Renewable Power Generation, Vol. 12, Issue 1, pp. 72-80, doi: 10.1049/iet-rpg.2017.0010 DOI: https://doi.org/10.1049/iet-rpg.2017.0010

Bogdanov, D., Ram, M., & Aghahosseini, A. E. (2021). Low-Cost Renewable Electricity as the Key Driver of the Global Energy Transition Towards Sustainability. Energy, Elsevier, Vol. 227(C), pp. 227. doi: 10.1016/j.energy.2021.120467 DOI: https://doi.org/10.1016/j.energy.2021.120467

Dan-Isa, A. & Kadandani, N.B. (2013). Assessment of Wind Energy Potential for Electricity Generation in Katsina, Nigeria. Bayero Journal of Engineering and Technology (BJET), Vol. 8 No. 2, pp. 36 – 42, Available Online at: https://bayerojet.com

Enerdata. (2017). Global Energy Statistic. https://yearbook.enerdata.net ,kkklll'p/{O>

Fathi, M., Khezri, R. Yazdani, A. & Mahmoudi, A. (2022). Comparative Study of Metaheuristic Algorithms for Optimal Sizing of Standalone Microgrids in a Remote Area Community. Neural Computing and Applications. 34(2): pp. 1-19, doi: 10.1007/s00521-021-06165-6. DOI: https://doi.org/10.1007/s00521-021-06165-6

Jayachandran M., & Ravi, G. (2017). Design and Optimization of Hybrid Micro-Grid System. 1st International Conference on Power Engineering, Computing and Control, PECCON. VIT University, Chennai Campus, Energy Procedia 117 (2017) pp. 95–103, doi: 10.1016/j.egypro.2017.05.111 DOI: https://doi.org/10.1016/j.egypro.2017.05.111

Kadandani, N.B. (2015). Grid Integration of Wind Farms for National Development, - A Case Study of Katsina Metropolis, Nigeria. Proceedings of North West University Faculty of Science 1st Annual International Conference, Kano, Nigeria, pp. 380 – 385

Kadandani, N.B., Hassan, S. & Abubakar I. (2023a). Solid State Transformer (SST) and the Challenges of the Future Grid. FUDMA Journal of Sciences (FJS), Vol. 7 No. 3, (Special Issue), pp. 330 – 335, doi: https://doi.org/10.33003/fjs-2023-0703-1965 DOI: https://doi.org/10.33003/fjs-2023-0703-1965

Kadandani, N.B., Hassan, S. & Abubakar I. (2023b). On the Suitability of Modular Multilevel Converter (MMC) in High Voltage Direct Current (HVDC) Transmission System. FUDMA Journal of Sciences (FJS), Vol. 7 No. 3, (Special Issue), pp. 318 - 323, doi: https://doi.org/10.33003/fjs-2023-0703-1963 DOI: https://doi.org/10.33003/fjs-2023-0703-1963

Li, J. W. Wei & Xiang, J. (2012). A Simple Sizing Algorithm for Stand-Alone PV/Wind/Battery Hybrid Microgrids. Energies, Vol. 5, Issue 12, pp. 5307-5323. doi: https://doi.org/10.3390/en5125307 DOI: https://doi.org/10.3390/en5125307

Liu, Y., Sun, J, Yu, H. Wang, Y. & Zhou, X.. (2019). An Improved Grey Wolf Optimizer Based on Differential Evolution and OTSU Algorithm, Applied Sciences, 10(18), 6343; https://doi.org/10.3390/app10186343 DOI: https://doi.org/10.3390/app10186343

Long, J. C. (2016). Improved Grey Wolf Optimization Algorithm for Constrained Mechanical Design Problems. Applied Mechanics and Materials, 851 pp. 553–558. doi: 10.4028/www.scientific.net/AMM.851.553 DOI: https://doi.org/10.4028/www.scientific.net/AMM.851.553

Mirjalili, S., Mirjalili, S.M. and Lewis, A. (2014) Grey Wolf Optimizer. Advances in Engineering Software, 69, pp. 46-61. doi: http://dx.doi.org/10.1016/j.advengsoft.2013.12.007. DOI: https://doi.org/10.1016/j.advengsoft.2013.12.007

Mousa, K., AlZu'bi, H. & Diabat A. (2010). Design of a Hybrid Solar-Wind Power Plant Using Optimization. Second International Conference on Engineering System Management and Application (ICESMA 2010), Sharjah, United Arab Emirates, pp. 1-6, Art. No. 5542695

Varetsky, Y. & Hanzelka, Z. (2015). Modeling Hybrid Renewable Energy System for Micro Grid. International Conference on Renewable Energies and Power Quality (ICREPQ’15). La Coruña (Spain), pp. 429-432, doi: https://doi.org/10.24084/repqj13.347 DOI: https://doi.org/10.24084/repqj13.347

Wang J.S., & Li S.X. (2019) An Improved Grey Wolf Optimizer Based on Differential Evolution and Elimination Mechanism. Sci Rep. 2019 May 9;9(1):7181. doi: 10.1038/s41598-019-43546-3. PMID: 31073211; PMCID: PMC6509275. DOI: https://doi.org/10.1038/s41598-019-43546-3

How to Cite
SesanB. S., AbubakarI., KadandaniN. B., & OlalekanI. B. (2024). OPTIMAL SIZING OF SOLAR-WIND HYBRID MICROGRID USING IMPROVED GREY WOLF OPTIMIZATION ALGORITHM A CASE STUDY OF KADUNA - NIGERIA. FUDMA JOURNAL OF SCIENCES, 7(6), 362 - 372. https://doi.org/10.33003/fjs-2023-0706-2214