Efficient energy management in microgrid using Zebra Optimization Algorithm

Abstract

The inherent variability of power output from photovoltaic (PV) systems, wind energy resources, battery energy storage systems (BESS), and hydrogen (H₂) fuel cells presents a significant challenge in efficiently integrating these technologies into microgrids. This stochastic nature underscores the necessity of accounting for fluctuations in renewable energy resources (RERs) to optimize energy utilization within the microgrid. This paper proposes a resource-efficient energy management (REEM) framework for a microgrid interconnected with the main power system. By dynamically regulating PV generation, wind power output, BESS discharge, and hydrogen fuel cell operation in response to load variations, the proposed approach enhances energy utilization and grid stability. To address the complexities associated with REEM, this study employs the zebra optimization algorithm (ZOA), a highly efficient metaheuristic technique. The primary objectives of this optimization include cost minimization, voltage profile enhancement, and optimal sizing of RERs. Simulation results demonstrate that the strategic integration of PV units, wind turbines, grid-connected BESS, and hydrogen fuel cells significantly reduces operational costs while improving overall system performance. Comparative analysis further reveals that ZOA outperforms the moth-flame optimization algorithm and stochastic fractal search network in achieving the defined optimization objectives.

Keywords

Energy resources / Photovoltaic system / Wind energy / Zebra Optimization Algorithm / Voltage stability / Cost function

Funding

None.

Conflict of interest

The authors declare that they have no competing interest.

References

[1] Us Salam I, Yousif M, Numan M, Billah M. Addressing the challenge of climate change: The role of microgrids in fostering a sustainable future - a comprehensive review. Renew Energy Focus. 2024; 48:100538. doi: 10.1016/j.ref.2024.100538

[2] Hasan M, Mifta Z, Salsabil NA, et al. A critical review on control mechanisms, supporting measures, and monitoring systems of microgrids considering large scale integration of renewable energy sources. Energy Rep. 2023; 10:4582-603. doi: 10.1016/j.egyr.2023.11.025

[3] Rajendran Pillai V, Rajasekharan Nair Valsala R, Raj V, Petra M, Krishnan Nair S, Mathew S. Exploring the potential of microgrids in the effective utilisation of renewable energy: A comprehensive analysis of evolving themes and future priorities using main path analysis. Designs. 2023; 7(3):58. doi: 10.3390/designs7030058

[4] Khan MW, Li G, Wang K, Numan M, Xiong L, Khan MA. Optimal control and communication strategies in multi-energy generation grid. IEEE Commun Surv Tutorials. 2023; 25(4):2599-2653. doi: 10.1109/COMST.2023.3304982

[5] Zhang H, Ma Y, Yuan K, Khayatnezhad M, Ghadimi N. Efficient design of energy microgrid management system: A promoted Remora optimization algorithm-based approach. Heliyon. 2024; 10(1):e23394. doi: 10.1016/j.heliyon.2023.e23394

[6] Hassan Q, Algburi S, Sameen AZ, Salman HM, Jaszczur M. A review of hybrid renewable energy systems: Solar and wind-powered solutions: Challenges, opportunities, and policy implications. Results Eng. 2023; 20:101621. doi: 10.1016/j.rineng.2023.101621

[7] Abo-Khalil AG, Sobhy A, Abdelkareem MA, Olabi AG. Advancements and challenges in hybrid energy storage systems: Components, control strategies, and future directions. Int J Thermofluids. 2023; 20:100477. doi: 10.1016/j.ijft.2023.100477

[8] Krishna VBM, Sandeep V. Experimental investigations on loading capacity and reactive power compensation of star configured three phase self excited induction generator for distribution power generation. Distrib Gene Altern Energy J. 2022; 37:725-748. doi: 10.13052/dgaej2156-3306.37316

[9] Vegunta SC, Higginson MJ, Kenarangui YE, et al. AC microgrid protection system design challenges-A practical experience. Energies. 2021; 14(7):2016. doi: 10.3390/en14072016

[10] Hamanah WM, Hossain MI, Shafiullah M, Abido MA. AC microgrid protection schemes: A comprehensive review. IEEE Access. 2023; 11:76842-76868. doi: 10.1109/ACCESS.2023.3298306

[11] Motjoadi V, Bokoro PN, Onibonoje MO. A review of microgrid-based approach to rural electrification in South Africa: Architecture and policy framework. Energies. 2020; 13(9):2193. doi: 10.3390/en13092193

[12] Pidikiti T, Shreedevi, Gireesha B, Subbarao M, Krishna VBM. Design and control of Takagi-Sugeno- Kang fuzzy based inverter for power quality improvement in grid-tied PV systems. Meas Sens. 2023; 25:100638. doi: 10.1016/j.measen.2022.100638

[13] Pagidela Y, Visali N. A short review on optimal allocation of microgrid. J Modern Technol. 2024; 11:132-140.

[14] Solanke AV, Verma SK, Kumar S, Oyinna B, Okedu KE. MPPT for hybrid energy system using machine learning techniques. J Modern Technol. 2024; 1(1):19-37.

[15] Ndeke CB, Adonis M, Almaktoof A. Energy management strategy for a hybrid micro-grid system using renewable energy. Discov Energy. 2024; 4(1):1. doi: 10.1007/s43937-024-00025-9

[16] Dey B, Misra S, Garcia Marquez FP. Microgrid system energy management with demand response program for clean and economical operation. Appl Energy. 2023; 334:120717. doi: 10.1016/j.apenergy.2023.120717

[17] Li T, Li Y, Li S, Zhang W. Research on current-limiting control strategy suitable for ground faults in AC microgrid. IEEE J Emerg Sel Top Power Electron. 2021; 9(2):1736-1750. doi: 10.1109/JESTPE.2020.2983726

[18] Ramos F, Pinheiro A, Nascimento R, et al. Development of operation strategy for battery energy storage system into hybrid AC microgrids. Sustainability. 2022; 14(21):13765. doi: 10.3390/su142113765

[19] Mohiuddin SM, Qi J. Optimal distributed control of AC microgrids with coordinated voltage regulation and reactive power sharing. IEEE Trans Smart Grid. 2022; 13(3):1789-1800. doi: 10.1109/TSG.2022.3147446

[20] Trojovská E, Dehghani M, Trojovský P. Zebra optimization algorithm: A new bio-inspired optimization algorithm for solving optimization algorithm. IEEE Access. 2022; 10:49445-49473. doi: 10.1109/ACCESS.2022.3172789

[21] Liu X, Wang JS, Zhang SB, Guan XY, Gao YZ. Optimization scheduling of off-grid hybrid renewable energy systems based on dung beetle optimizer with convergence factor and mathematical spiral. Renew Energy. 2024; 237:121874. doi: 10.1016/j.renene.2024.121874

[22] Ashetehe AA, Shewarega F, Bantyirga B, et al. Optimal design of off-grid hybrid system using a new zebra optimization and stochastic load profile. Sci Rep. 2025; 14:29255. doi: 10.1038/s41598-024-80558-0

[23] Habib HUR, Subramaniam U, Waqar A, Farhan BS, Kotb KM, Wang S. Energy cost optimization of hybrid renewables based V2G microgrid considering multi objective function by using artificial bee colony optimization. IEEE Access. 2020; 8:62076-62093. doi: 10.1109/ACCESS.2020.2984537

[24] Zhou B, Zou J, Chung CY, et al. Multi-microgrid energy management systems: Architecture, communication, and scheduling strategies. J Modern Power Syst Clean Energy. 2021; 9(3):463-476. doi: 10.35833/MPCE.2019.000237

[25] Roslan MF, Hannan MA, Jern Ker P, Begum RA, Indra Mahlia T, Dong ZY. Scheduling controller for microgrids energy management system using optimization algorithm in achieving cost saving and emission reduction. Appl Energy. 2021; 292:116883. doi: 10.1016/j.apenergy.2021.116883

[26] Kishore PM, Ravikumar B. Refined hybrid microgrid architecture for the improvement of voltage profile. Energy Proc. 2016; 90:645-654. doi: 10.1016/j.egypro.2016.11.233

[27] Paredes LA, Molina MG, Serrano BR. Enhancing dynamic voltage stability in resilient microgrids using FACTS devices. IEEE Access. 2023; 11:66150-66176. doi: 10.1109/ACCESS.2023.3291009

[28] Hossain E, Perez R, Nasiri A, Bayindir R. Stability improvement of microgrids in the presence of constant power loads. Int J Electr Power Energy Syst. 2018; 96:442-456. doi: 10.1016/j.ijepes.2017.10.016

[29] Paredes LA, Molina MG, Serrano BR. Improvements in the Voltage Stability of a Microgrid Due to Smart FACTS-an Approach from Resilience. In: 2020 IEEE ANDESCON; 2020. p. 1-6. doi: 10.1109/ANDESCON50619.2020.9272006

[30] Mohapatra S, Mohapatra P. American zebra optimization algorithm for global optimization problems. Sci Rep. 2023; 13(1):5211. doi: 10.1038/s41598-023-31876-2