Abstract
This research integrates renewable energy resources into microgrid systems to address cost, emissions, and reliability concerns. Employing multi-objective optimization, the Teaching-Learning-Based Optimization (TLBO) algorithm emerges as highly effective, achieving substantial cost and greenhouse gas reductions. TLBO showcases rapid convergence and superior performance for the proposed microgrid architecture, offering valuable insights for sustainable energy planning. The proposed microgrid architecture includes micro-turbine (MT), a solar photovoltaic (PV) system, a wind turbine (WT), and a battery energy storage system (BESS). The numerical results of the proposed system compared with load supplied by main grid. The achieved cost savings of the proposed system is 66.85 % and GHG cost savings is 67.77% compared to load supplied by grid.
| Original language | English |
|---|---|
| Journal | Proceedings of the International Conference on Power Electronics, Drives, and Energy Systems for Industrial Growth, PEDES |
| Issue number | 2024 |
| DOIs | |
| Publication status | Published - 2024 |
| Event | 11th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2024 - Mangalore, India Duration: 18 Dec 2024 → 21 Dec 2024 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy -
SDG 13 Climate Action
Keywords
- CO2 Emissions
- Energy management
- Microgrid
- Optimization
ASJC Scopus subject areas
- Safety, Risk, Reliability and Quality
- Energy Engineering and Power Technology
- Mechanical Engineering
- Electrical and Electronic Engineering
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