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Multi-objective optimization framework for dynamic energy management in hybrid microgrids using NSGA-III
Mohd Bilal1, Arshad Mohammad2, Imdadullah3
1Department of Electrical Engineering, Aligarh Muslim University, Aligarh, India.
Scientific Reports
|July 17, 2026
Summary
This study introduces an optimization framework for hybrid microgrids, enhancing demand-side management by minimizing costs and peak-to-average ratio using advanced algorithms like NSGA-III for better renewable energy integration and operational flexibility.
Area of Science:
- Electrical Engineering
- Renewable Energy Systems
- Optimization Theory
Background:
- Hybrid microgrids integrate diverse energy sources (PV, WT, BESS, FC) with grid connections for enhanced reliability and efficiency.
- Demand-side management (DSM) is crucial for optimizing energy consumption and reducing operational costs, especially under real-time pricing (RTP).
- Traditional optimization methods often struggle with multi-objective problems, requiring predefined weights that may not capture optimal trade-offs.
Purpose of the Study:
- To develop a comprehensive multi-objective optimization framework for DSM in hybrid microgrids.
- To simultaneously minimize Peak-to-Average Ratio (PAR) and total operating cost.
- To evaluate the framework's performance across different hybrid microgrid configurations.
Main Methods:
- Dynamic load scheduling under real-time pricing (RTP).
- Prioritization of renewable energy dispatch and intelligent battery management.
- Application of the Non-dominated Sorting Genetic Algorithm III (NSGA-III) for Pareto front generation without predefined objective weights.
- Evaluation under three system configurations: grid-only, grid-integrated renewables/battery, and grid-integrated renewables/battery/fuel cell.
Main Results:
- Hybrid renewable energy configurations significantly outperform grid-only operation in economic and operational aspects.
- The NSGA-III framework successfully generated Pareto-optimal strategies, revealing trade-offs between operating cost and PAR.
- A minimum-cost solution achieved 131.73 Cents, and a compromise solution achieved 155.98 Cents with improved DSM.
- NSGA-III demonstrated superior performance over NSGA-II, MOPSO, SPEA2, and Weighted Sum Method in terms of Pareto front quality, convergence, diversity, and robustness.
Conclusions:
- The proposed NSGA-III based optimization framework is effective for multi-objective energy management in hybrid microgrids.
- The framework offers a scalable solution for improving economic efficiency, operational flexibility, and sustainability of smart microgrids.
- Hybrid microgrid configurations with renewables and storage provide substantial benefits over conventional grid-dependent systems.
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