Multi-objective optimization of hybrid renewable microgrids integrating solar, wind, and biomass for rural electrification

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Article ID: 635
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DOI:

https://doi.org/10.18686/cest635

Keywords:

hybrid renewable microgrid; multi-objective optimization; NSGA-II; solar-wind-biomass integration; rural electrification

Abstract

 Rural electrification in developing regions requires decentralized, sustainable energy systems that balance cost, reliability, and environmental performance. Hybrid renewable microgrids integrating solar, wind, and biomass have been studied extensively. However, existing methods frequently rely on generic component models and simplified operational methods, limiting their applicability to region-specific conditions. This study addresses these limitations through three novel contributions. (a) It develops Tamil Nadu-specific biomass feedstock modelling that incorporates seasonal agricultural residue availability and local gasification characteristics. (b) It implements integrated sizing and operation optimization using hourly dispatch decisions within the Non-dominated Sorting Genetic Algorithm II (NSGA-II). (c) It conducts a comprehensive 6-parameter sensitivity analysis to quantify model robustness under realistic uncertainty. The model optimizes hybrid microgrids integrating solar photovoltaic, wind turbine, biomass gasifier, and lithium-ion battery subsystems. Three conflicting objectives are minimized: Levelized Cost of Energy (LCOE), Loss of Power Supply Probability (LPSP), and carbon dioxide emissions. A case study of 350 rural households in Tamil Nadu validates the approach using hourly meteorological and load data with regionally calibrated techno-economic parameters. Results prove that hybrid configurations substantially outperform single-source systems across all metrics. Pareto-optimal solutions reveal critical trade-offs between economic, technical, and environmental objectives. Sensitivity analysis identifies demand growth, wind variability, and battery efficiency as dominant drivers of model robustness, while financial parameters primarily influence cost feasibility. The results validate region-specific hybrid microgrid optimization as a technically and economically viable pathway for sustainable rural electrification, providing policymakers with actionable insights on system sizing, resource management, and investment prioritization.

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2026-02-13

How to Cite

M. Ali, H., Choudhary, P. K., Lazer, A. J. P., Nuthakki, P., Smerat, A., Sunderaraj, N., Sabirov, S., & Sengan, S. (2026). Multi-objective optimization of hybrid renewable microgrids integrating solar, wind, and biomass for rural electrification. Clean Energy Science and Technology, 4(1). https://doi.org/10.18686/cest635

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Vol. 4 No. 1 (2026)

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Article

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Copyright (c) 2026 Hayder M. Ali, Prashant Kumar Choudhary, Arokia Jesu Prabhu Lazer, Praveena Nuthakki, Aseel Smerat, Nivetha Sunderaraj, Sardor Sabirov, Sudhakar Sengan

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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