Multi-objective optimization framework for dynamic energy management in hybrid microgrids using NSGA-III
5 affiliations
- 1Department of Electrical Engineering, Aligarh Muslim University
- 2Department of Electrical and Electronics Engineering, Maulana Azad College of Engineering and Technology (MACET)
- 3Electrical Engineering Section, University Polytechnic, Aligarh Muslim University
- 4Interdisciplinary Research Center for Sustainable Energy Systems, Research and Innovation, King Fahd University of Petroleum & Minerals
- 5Department of Electrical and Electronics Engineering, School of Engineering and Sciences, SRM University AP
Abstract
This study proposes a comprehensive multi-objective optimization framework for demand-side management of a hybrid microgrid comprising photovoltaic (PV) panels, wind turbines (WT), a battery energy storage system (BESS), a fuel cell (FC), and a grid connection. The framework simultaneously minimizes the Peak-to-Average Ratio (PAR) and total operating cost through dynamic load scheduling under real-time pricing (RTP). A renewable energy utilization strategy prioritizes clean energy dispatch, while an intelligent battery management scheme optimizes charging and discharging decisions according to renewable generation availability, load demand, and electricity price signals. To address the limitations of conventional weighted-sum optimization approaches, the Non-dominated Sorting Genetic Algorithm III (NSGA-III) is employed to generate a diverse and well-distributed Pareto front without requiring predefined objective weights. The proposed framework is evaluated under three energy system configurations: (i) grid-only operation, (ii) grid-integrated renewable energy and battery storage, and (iii) grid-integrated renewable energy, battery storage, and fuel-cell support. The results demonstrate that hybrid renewable energy configurations significantly improve both economic and operational performance compared with conventional grid-dependent operation. The proposed framework generated multiple Pareto-optimal operating strategies with different trade-offs between operating cost and PAR. The minimum-cost solution achieved an operating cost of 131.73 Cents, while a representative compromise solution achieved 155.98 Cents with improved demand-side management performance. Comparative evaluation against NSGA-II, MOPSO, SPEA2, and the Weighted Sum Method reveals that NSGA-III consistently achieves superior Pareto-front quality, convergence characteristics, solution diversity, and robustness across 30 independent trials. The findings demonstrate the effectiveness of NSGA-III for multi-objective energy management and provide a scalable optimization framework for enhancing the economic efficiency, operational flexibility, and sustainability of future smart microgrid systems.
Related
- Multi-objective optimization of injection molding process parameters for thin-walled shell parts based on a RIME-RF-MOGWO framework
- A comparative study of FLC and MPC strategies for battery-protective energy management in grid-connected DC microgrids with hybrid renewable sources
- 64 Explainable AI for objective immunohistochemistry antibody clone selection, failure attribution, and optimization
- Adaptive dual-space discriminator for large-scale multi-objective optimization
- Multi-objective optimization of a hybrid photovoltaic–thermal energy system under load demand uncertainties using Bat algorithm
BibTeX
@article{bilal2026multiobjective,
title = {{Multi-objective optimization framework for dynamic energy management in hybrid microgrids using NSGA-III}},
author = {Mohd Bilal and Arshad Mohammad and Imdadullah and Salman Hameed and Fareed Ahmad and Tousif Khan Nizami},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-62239-2},
url = {https://doi.org/10.1038/s41598-026-62239-2},
note = {CC BY},
}
RIS
TY - JOUR
TI - Multi-objective optimization framework for dynamic energy management in hybrid microgrids using NSGA-III
AU - Mohd Bilal
AU - Arshad Mohammad
AU - Imdadullah
AU - Salman Hameed
AU - Fareed Ahmad
AU - Tousif Khan Nizami
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-62239-2
UR - https://doi.org/10.1038/s41598-026-62239-2
AB - This study proposes a comprehensive multi-objective optimization framework for demand-side management of a hybrid microgrid comprising photovoltaic (PV) panels, wind turbines (WT), a battery energy storage system (BESS), a fuel cell (FC), and a grid connection. The framework simultaneously minimizes the Peak-to-Average Ratio (PAR) and total operating cost through dynamic load scheduling under real-time pricing (RTP). A renewable energy utilization strategy prioritizes clean energy dispatch, while an intelligent battery management scheme optimizes charging and discharging decisions according to renewable generation availability, load demand, and electricity price signals. To address the limitations of conventional weighted-sum optimization approaches, the Non-dominated Sorting Genetic Algorithm III (NSGA-III) is employed to generate a diverse and well-distributed Pareto front without requiring predefined objective weights. The proposed framework is evaluated under three energy system configurations: (i) grid-only operation, (ii) grid-integrated renewable energy and battery storage, and (iii) grid-integrated renewable energy, battery storage, and fuel-cell support. The results demonstrate that hybrid renewable energy configurations significantly improve both economic and operational performance compared with conventional grid-dependent operation. The proposed framework generated multiple Pareto-optimal operating strategies with different trade-offs between operating cost and PAR. The minimum-cost solution achieved an operating cost of 131.73 Cents, while a representative compromise solution achieved 155.98 Cents with improved demand-side management performance. Comparative evaluation against NSGA-II, MOPSO, SPEA2, and the Weighted Sum Method reveals that NSGA-III consistently achieves superior Pareto-front quality, convergence characteristics, solution diversity, and robustness across 30 independent trials. The findings demonstrate the effectiveness of NSGA-III for multi-objective energy management and provide a scalable optimization framework for enhancing the economic efficiency, operational flexibility, and sustainability of future smart microgrid systems.
SN - 2045-2322
ER -