DOI: https://doi.org/10.61435/ijred.2026.62158
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Development of hybrid COA-WCA multi-objective optimization of microgrid hybrid renewable energy system: A case study of Nusa Penida

1Study Program of Doctoral Engineering Science (PSDIT), Udayana University, Bali, Indonesia

2Department of Electrical Engineering, Politeknik Negeri Bali (PNB), Bali, Indonesia

Received: 26 Dec 2025; Revised: 17 Apr 2026; Accepted: 7 May 2026; Available online: 14 May 2026; Published: 1 Jul 2026.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2026 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract

This study proposes a hybrid metaheuristic that combines the Coyote Optimization Algorithm (COA) and the Water Cycle Algorithm (WCA) to improve multi-objective optimization of a hybrid renewable energy microgrid (HRES) for Nusa Penida Island. Such islanded systems must balance investment cost, operational reliability, and renewable curtailment while facing stochastic weather and demand. The optimization therefore targets simultaneous minimization of cost of energy (COE), loss of power supply probability (LPSP), and dummy load (DL) as key indicators of affordability, adequacy, and energy-utilization efficiency. A sequential Hybrid COA–WCA framework is implemented using an annual time-series of electrical demand and local renewable-resource profiles. Candidate solutions encode the main HRES components, including photovoltaic generation, wind generation, battery storage, and conventional backup biodiesel generation, while respecting practical operating limits. Multi-objective optimization is handled using a weighted-sum formulation, subject to standard power-balance, component-operating, and reliability constraints. The proposed approach is benchmarked against standalone COA, WCA, and WOA under multiple uncertainty scenarios that perturb techno-economic parameters and resource–load conditions. The Hybrid COA–WCA achieved the lowest mean objective value (mean f = 0.87274; min = 0.82136; max = 0.92409) and the best final-iteration mean of 0.87258 compared with COA (0.87303), WCA (0.87367), and WOA (0.87756). The optimized design delivered COE = 1.388, LPSP = 0.03180, and DL = 6,609.025 on average across scenarios. Robustness analysis also indicates faster stabilization and the smallest end-of-run fluctuation range (0.82136–0.92409), confirming improved convergence stability relative to the benchmark algorithms. Overall, the Hybrid COA–WCA provides a stable and competitive optimization approach for HRES sizing under uncertainty, yielding consistently high-quality solutions that support planning and decision-making for island microgrids.

Keywords: cost of energy; dummy load; hybrid renewable energy systems; loss of power supply probability; multi-objective optimization; nusa penida

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Section: Original Research Article
Language : EN
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  1. Akinwola, A.B. and Alkuhayli, A. (2025) Hybrid PSO–Reinforcement Learning-Based Adaptive Virtual Inertia Control for Frequency Stability in Multi-Microgrid PV Systems, Electronics (Switzerland), 14(17). https://doi.org/10.3390/electronics14173349
  2. Akter, H., Howlader, H. O. R., Saber, A. Y., Mandal, P., Takahashi, H., & Senjyu, T. (2021) Optimal sizing of hybrid microgrid in a remote island considering advanced direct load control for demand response and low carbon emission, Energies, 14(22). https://doi.org/10.3390/en14227599
  3. Alahakoon, S., Roy, R.B. and Arachchillage, S.J. (2023) Optimizing Load Frequency Control in Standalone Marine Microgrids Using Meta-Heuristic Techniques, Energies, 16(13), 4846. https://doi.org/10.3390/en16134846
  4. Alhawsawi, E.Y., Salhein, K. and Zohdy, M.A. (2024) A Comprehensive Review of Existing and Pending University Campus Microgrids, Energies . https://doi.org/10.3390/en17102425
  5. Aljohani, T.M., Ebrahim, A.F. and Mohammed, O. (2020) Hybrid microgrid energy management and control based on metaheuristic-driven vector-decoupled algorithm considering intermittent renewable sources and electric vehicles charging lot, Energies, 13(13). https://doi.org/10.3390/en13133423
  6. Alsharif, A., Ahmed, A., Khaleel, M.M., Nassar, Y., Sharif, A., and El-Khozondar, H.J. (2023) Whale Optimization Algorithm for Renewable Energy Sources Integration Considering Solar-to-Vehicle Technology, Proceedings of 2023 IEEE 9th International Women in Engineering (WIE) Conference on Electrical and Computer Engineering, WIECON-ECE 2023. https://doi.org/10.1109/WIECON-ECE60392.2023.10456379
  7. Rajagopalan, A., Nagarajan, K., Bajaj, M. (2024. (2024) Multi-Objective Energy Management in a Renewable and EV-integrated Microgrid Using an Iterative Map-Based Self-Adaptive Crystal Structure Algorithm, Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-66644-3
  8. Azeem, O., Ali, M., Abbas, G., Uzair, M., Qahmash, A., Algarni, A., & Hussain, M. R. (2021) A comprehensive review on integration challenges, optimization techniques and control strategies of hybrid ac/dc microgrid, Applied Sciences (Switzerland), 11(14). https://doi.org/10.3390/app11146242
  9. Azeem, S. Javed, I. Naseer, O. Ali and Ghazal, T.M. (2025) A New Hybrid PSO-HHO Wrapper Based Optimization for Feature Selection, IEEE Access, 13. https://doi.org/10.1109/ACCESS.2025.3570901
  10. Bakeer, A., Elmorshedy, M.F., Salama, H. (2023) Optimal design and performance analysis of coastal microgrid using different optimization algorithms, Electrical Engineering, 105(6). https://doi.org/10.1007/s00202-023-01954-9
  11. Balasubramanyam, P. and Sood, V.K. (2023) A Novel Hybrid Swarm Intelligence and Cuckoo Search Based Microgrid EMS for Optimal Energy Scheduling, Distributed Generation and Alternative Energy Journal, 38(4). https://doi.org/10.13052/dgaej2156-3306.3843
  12. Bazgan, C., Ruzika, S., Thielen, C. (2022) The Power of the Weighted Sum Scalarization for Approximating Multiobjective Optimization Problems, Theory of Computing Systems, 66(1). https://doi.org/10.1007/s00224-021-10066-5
  13. Belboul, Z., Toual, B., Kouzou, A., Mokrani, L., Bensalem, A., Kennel, R., & Abdelrahem, M. (2022) Multiobjective Optimization of a Hybrid PV/Wind/Battery/Diesel Generator System Integrated in Microgrid: A Case Study in Djelfa, Algeria, Energies, 15(10), 3579. Available at: https://doi.org/10.3390/en15103579
  14. Bhatti, M. Z. A., Siddique, A., Aslam, W., & Atiq, S. (2024) Design and Analysis of a Hybrid Stand-Alone Microgrid, Energies, 17(1). https://doi.org/10.3390/en17010200
  15. Billah, M., Yousif, M. Numan, M., Salam, I.U., Kazmi, S.A.A, and Alghamdi, T.A.H. (2023) Decentralized Smart Energy Management in Hybrid Microgrids: Evaluating Operational Modes, Resources Optimization, and Environmental Impacts, IEEE Access, 11. https://doi.org/10.1109/ACCESS.2023.3343466
  16. Bose, R.B. and Auxillia, D.J. (2021) A robust predictive feedback FPID controller using elephant herd virtual inertia optimization control algorithm in Islanded microgrid, International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 34(5). https://doi.org/10.1002/jnm.2889
  17. Boukaibat, A., Krami, N., Rochdi, Y., El Bakkali, Y., Laamim, M., & Rochd, A. (2025) Real-Time Energy Management in Microgrids: Integrating T-Cell Optimization, Droop Control, and HIL Validation with OPAL-RT, Energies, 18(15). https://doi.org/10.3390/en18154035
  18. Cetinbas, I., Tamyurek, B. and Demirtas, M. (2022) The Hybrid Harris Hawks Optimizer-Arithmetic Optimization Algorithm: A New Hybrid Algorithm for Sizing Optimization and Design of Microgrids, IEEE Access, 10. https://doi.org/10.1109/ACCESS.2022.3151119
  19. Charadi, S., Chakir, H. E., Redouane, A., El Hasnaoui, A., & El Bhiri, B. (2023) A Novel Hybrid Imperialist Competitive Algorithm–Particle Swarm Optimization Metaheuristic Optimization Algorithm for Cost-Effective Energy Management in Multi-Source Residential Microgrids, Energies, 16(19), 6896. https://doi.org/10.3390/en16196896
  20. Charadi, S., Chakir, H. E., Redouane, A., El Hasnaoui, A., Et-taoussi, M. (2023) Bi-objective optimal active and reactive power flow management in grid-connected AC/DC hybrid microgrids using metaheuristic-PSO, Clean Energy, 7(6), 1356–1380. https://doi.org/10.1093/ce/zkad081
  21. CORE Udayana (2024) Pemetaan Potensi untuk Nusa Penida 100% Energi Terbarukan
  22. Dabhi, D. and Pandya, K. (2020) Metaheuristic Optimization Algorithm for Day-Ahead Energy Resource Management (ERM) in Microgrid Environment of Power System, Lecture Notes in Electrical Engineering. https://doi.org/10.1007/978-981-15-0974-2_11
  23. Pham, M.C., Tran, T.Q., Bacha, S., Hably, A., and An, L.N. (2018) Optimal sizing of battery energy storage system for an islaned microgrid, Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, 1, 1899–1903. https://doi.org/10.1109/IECON.2018.8591391
  24. Dey, S. and Xu, H. (2023) Intelligent Distributed Swarm Control for Large-Scale Multi-UAV Systems: A Hierarchical Learning Approach, Electronics (Switzerland), 12(1). https://doi.org/10.3390/electronics12010089
  25. Diab, A.A.Z, Sultan, H.M., Mohamed, I.S., Kuznetsov, O.N., and Do, T.D. (2019) Application of Different Optimization Algorithms for Optimal Sizing of PV/Wind/Diesel/Battery Storage Stand-Alone Hybrid Microgrid, Ieee Access, 7, 119223–119245. https://doi.org/10.1109/access.2019.2936656
  26. Mohamed, M.A.E., Mahmoud, A.M., Saied, E.M.M. (2024) Hybrid Cheetah Particle Swarm Optimization Based Optimal Hierarchical Control of Multiple Microgrids, Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-59287-x
  27. Eskandar, H., Sadollah, A., Bahreininejad, A., Hamdi, M. (2012) Water cycle algorithm - A novel metaheuristic optimization method for solving constrained engineering optimization problems, Computers and Structures, 110–111. https://doi.org/10.1016/j.compstruc.2012.07.010
  28. Fracas, P., Zondervan, E., Franke, M., Camarda, K., Valtchev, S., & Valtchev, S. (2022) Techno-Economic Optimization Study of Interconnected Heat and Power Multi-Microgrids with a Novel Nature-Inspired Evolutionary Method, Electronics (Switzerland), 11(19). https://doi.org/10.3390/electronics11193147
  29. Helfrich, S., Herzel, A., Ruzika, S. . (2022) An approximation algorithm for a general class of multi-parametric optimization problems, Journal of Combinatorial Optimization, 44(3). https://doi.org/10.1007/s10878-022-00902-w
  30. Hossain, M.A., Ahmed, T., Hossain, M.S., Dey, P., Ahmed, S., Hossain, M. M. (2022) Optimization of the factors affecting BT-2 black tea fermentation by observing their combined effects on the quality parameters of made tea using Response Surface Methodology (RSM), Heliyon, 8(2). https://doi.org/10.1016/j.heliyon.2022.e08948
  31. Hossam-Eldin, A., Mostafa, H., Kotb, H., AboRas, K. M., Selim, A., & Kamel, S. (2022) Improving the Frequency Response of Hybrid Microgrid Under Renewable Sources’ Uncertainties Using a Robust LFC-Based African Vulture Optimization Algorithm, Processes, 10(11), 2320. https://doi.org/10.3390/pr10112320
  32. Hussein, H. M., Rafin, S. M. S. H., Abdelrahman, M. S., & Mohammed, O. A.. (2024) Hardware Implementation of a Resilient Energy Management System for Networked Microgrids, World Electric Vehicle Journal, 15(5). https://doi.org/10.3390/wevj15050209
  33. Ibrahim, I.M., Abdelaziz, A.Y., H. H. Alhelou, H.H., and Omran, W.A. (2023) Sizing of Microgrid System Including Multi-Functional Battery Storage and Considering Uncertainties, Ieee Access, 11, 29521–29540. https://doi.org/10.1109/access.2023.3259459
  34. Jaiswal, S., Sahoo, S.C. and Das, D.C. (2025) Microgrids integrated with diverse energy storage technologies, its challenges and optimized applications: A review, Suranaree Journal of Science and Technology, 32(2). https://doi.org/10.55766/sujst7894
  35. Janamala, V. and Sreenivasulu Reddy, D. (2021) Coyote optimization algorithm for optimal allocation of interline –Photovoltaic battery storage system in islanded electrical distribution network considering EV load penetration, Journal of Energy Storage, 41. https://doi.org/10.1016/j.est.2021.102981
  36. Kharrich, M., Mohammed, O. H., Kamel, S., Selim, A., Sultan, H. M., Akherraz, M., & Jurado, F. (2020) Development and Implementation of a Novel Optimization Algorithm for Reliable and Economic Grid-Independent Hybrid Power System, Applied Sciences, 10(18), p. 6604. Available at: https://doi.org/10.3390/app10186604
  37. Lei, Y., Wang, Z., Wang, D.. (2023) Co-benefits of carbon neutrality in enhancing and stabilizing solar and wind energy, Nature Climate Change, 13(7). https://doi.org/10.1038/s41558-023-01692-7
  38. Marler, R.T. and Arora, J.S. (2010) The weighted sum method for multi-objective optimization: New insights, Structural and Multidisciplinary Optimization, 41(6). https://doi.org/10.1007/s00158-009-0460-7
  39. Mirjalili, S. and Lewis, A. (2016) The Whale Optimization Algorithm, Advances in Engineering Software, 95, 51–67. https://doi.org/10.1016/J.ADVENGSOFT.2016.01.008
  40. Mirtaheri, H., Macaluso, P., Fantino, M., Efstratiadi, M., Tsakanikas, S., Papadopoulos, P., & Mazza, A. (2021) Hybrid forecast and control chain for operation of flexibility assets in micro-grids, Energies, 14(21). https://doi.org/10.3390/en14217252
  41. Mokhtara, C., Negrou, B., Settou, N., Settou, B., Samy, M.M. (2021) 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. https://doi.org/10.1016/j.energy.2020.119605
  42. Mquqwana, M.A. and Krishnamurthy, S. (2024) Particle Swarm Optimization for an Optimal Hybrid Renewable Energy Microgrid System under Uncertainty, Energies, 17(2). https://doi.org/10.3390/en17020422
  43. Naseri, N., El Hani, S., Machmoum, M., Elbouchikhi, E., & Daghouri, A. (2024) Energy Management Strategy for a Net Zero Emission Islanded Photovoltaic Microgrid-Based Green Hydrogen System, Energies, 17(9). https://doi.org/10.3390/en17092111
  44. Nasir, M., Sadollah, A., Choi, Y.H. al. (2020) A comprehensive review on water cycle algorithm and its applications, Neural Computing and Applications. https://doi.org/10.1007/s00521-020-05112-1
  45. Pierezan, J. and Dos Santos Coelho, L. (2018) Coyote Optimization Algorithm: A New Metaheuristic for Global Optimization Problems, 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings. https://doi.org/10.1109/CEC.2018.8477769
  46. Sanajaoba Singh, S. and Fernandez, E. (2018) Modeling, size optimization and sensitivity analysis of a remote hybrid renewable energy system, Energy, 143. https://doi.org/10.1016/j.energy.2017.11.053
  47. Senarathna, T.S.S. and Hemapala, K.T.M.U. (2020) Optimized Adaptive Overcurrent Protection Using Hybridized Nature-Inspired Algorithm and Clustering in Microgrids, Energies, 13(13). https://doi.org/10.3390/en13133324
  48. Sharma, D.K. and Prabhakar, A. (2021) Orthogonal array optimization of the operational parameters for air-cooled cylindrical lithium-ion battery module, International Symposium on Advances in Computational Heat Transfer. https://doi.org/10.1615/ichmt.2021.cht-21.180
  49. Singla, M. K., Gupta, J., Alsharif, M. H., & Jahid, A. (2023) Optimizing Integration of Fuel Cell Technology in Renewable Energy-Based Microgrids for Sustainable and Cost-Effective Energy, Energies, 16(11), 4482. https://doi.org/10.3390/en16114482
  50. Spiegel, M.H. and Strasser, T.I. (2023) A testbed-based approach for the resilience assessment of multi-microgrids, Elektrotechnik und Informationstechnik, 140(1). https://doi.org/10.1007/s00502-022-01093-2
  51. Tarife, R., Nakanishi, Y., Chen, Y., Zhou, Y., Estoperez, N., & Tahud, A. (2022) Optimization of Hybrid Renewable Energy Microgrid for Rural Agricultural Area in Southern Philippines, Energies, 15(6). https://doi.org/10.3390/en15062251
  52. Tjahjana,D.D.D.P., Rachmanto, R.A., Juwana, W.E., Prasojo, Y.J., Prasetyo, S.D, Arifin, Z. (2023) Economic Feasibility of a PV-Wind Hybrid Microgrid System for Off-Grid Electrification in Papua, Indonesia, International Journal of Design and Nature and Ecodynamics, 18(4). https://doi.org/10.18280/ijdne.180407
  53. Tripathi,M., Pathak, N., Chaudhary, V.K., Singh, P., Singh, P.K, Thirumalesh, B.V., Saroj Bala, S., Maurya, A.K, Patel,N, Yadav, B.K.. (2023) Microbial Decolorization of Crystal Violet Dye by a Native Multi-Metal Tolerant Aeromonas caviae MT-1 Isolate from Dye-Contaminated Soil: Optimization and Phytotoxicity Study, Toxicology International, 30(1). https://doi.org/10.18311/ti/2023/v30i1/31254
  54. Twala, S., Ye, X., Xia, X., Zhang, L. (2023) Optimal integration of solar home systems and appliance scheduling for residential homes under severe national load shedding, Journal of Automation and Intelligence, 2(4). Available at: https://doi.org/10.1016/j.jai.2023.12.001
  55. Ullah, K., Jiang, Q., Geng, G., Rahim, S., & Khan, R. A. (2022) Optimal Power Sharing in Microgrids Using the Artificial Bee Colony Algorithm, Energies, 15(3), 1067. https://doi.org/10.3390/en15031067
  56. Ullah, Z., Wang, S., Elkadeem, M., R., and Kotb, K. M. (2023) Optimal Capacity Planning and Analysis of a Sustainable Solar/Wind Microgrid in Rural Areas, IEEE Green Technologies Conference. https://doi.org/10.1109/GreenTech56823.2023.10173808
  57. Upadhyay, S.K. and Bhasker, R. (2025) Cheetah optimization based ANN maximum power point tracking for PV and wind in hybrid AC-DC microgrid, Engineering Research Express, 7(2). https://doi.org/10.1088/2631-8695/ade5ee
  58. Wang, Y., He, X., Liu, Q., Razmjooy, S. (2024) Economic and technical analysis of an HRES (Hybrid Renewable Energy System) comprising wind, PV, and fuel cells using an improved subtraction-average-based optimizer, Heliyon, 10(12). https://doi.org/10.1016/j.heliyon.2024.e32712
  59. Williams, M.J. and Chang, C.K. (2024) The Optimal Integration of Virtual Power Plants for the South African National Grid Based on an Energy Mix as per the Integrated Resource Plan 2019: A Review, Energies. https://doi.org/10.3390/en17246489
  60. Zaheri, S.H., Hosseini, M. and Fathinasab, M. (2024) Well Pattern optimization as a planning process using a novel developed optimization algorithm, Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-78196-7
  61. Zhong, M., Wang, L., Liu, Z. and Hou, S. (2020) Reliability Evaluation and Improvement of Islanded Microgrid Considering Operation Failures of Power Electronic Equipment,Journal of Modern Power Systems and Clean Energy, 8(1). https://doi.org/10.35833/MPCE.2018.000666

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