Optimizing Renewable Microgrid Performance Through Hydrogen Storage Integration
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| Veröffentlicht in: | Algorithms vol. 18, no. 10 (2025), p. 656-675 |
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MDPI AG
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| Online-Zugang: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3265821488 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1999-4893 | ||
| 024 | 7 | |a 10.3390/a18100656 |2 doi | |
| 035 | |a 3265821488 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231333 |2 nlm | ||
| 100 | 1 | |a Ribeiro, Bruno |u Department of Engineering, University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal; baptista@utad.pt | |
| 245 | 1 | |a Optimizing Renewable Microgrid Performance Through Hydrogen Storage Integration | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The global transition to a low-carbon energy system requires innovative solutions that integrate renewable energy production with storage and utilization technologies. The growth in energy demand, combined with the intermittency of these sources, highlights the need for advanced management models capable of ensuring system stability and efficiency. This paper presents the development of an optimized energy management system integrating renewable sources, with a focus on green hydrogen production via electrolysis, storage, and use through a fuel cell. The system aims to promote energy autonomy and support the transition to a low-carbon economy by reducing dependence on the conventional electricity grid. The proposed model enables flexible hourly energy flow optimization, considering solar availability, local consumption, hydrogen storage capacity, and grid interactions. Formulated as a Mixed-Integer Linear Programming (MILP) model, it supports strategic decision-making regarding hydrogen production, storage, and utilization, as well as energy trading with the grid. Simulations using production and consumption profiles assessed the effects of hydrogen storage capacity and electricity price variations. Results confirm the effectiveness of the model in optimizing system performance under different operational scenarios. | |
| 651 | 4 | |a Portugal | |
| 653 | |a Fuel cells | ||
| 653 | |a Hydrogen storage | ||
| 653 | |a Linear programming | ||
| 653 | |a Green hydrogen | ||
| 653 | |a Hydrogen production | ||
| 653 | |a Distributed generation | ||
| 653 | |a Energy flow | ||
| 653 | |a Integer programming | ||
| 653 | |a Emissions | ||
| 653 | |a Optimization | ||
| 653 | |a Clean energy | ||
| 653 | |a Air pollution | ||
| 653 | |a Storage capacity | ||
| 653 | |a Heat | ||
| 653 | |a Electric power grids | ||
| 653 | |a Outdoor air quality | ||
| 653 | |a Carbon | ||
| 653 | |a Efficiency | ||
| 653 | |a Electricity consumption | ||
| 653 | |a Mathematical programming | ||
| 653 | |a Electrolytes | ||
| 653 | |a Electricity | ||
| 653 | |a Electrolysis | ||
| 653 | |a Renewable resources | ||
| 653 | |a Carbon dioxide | ||
| 653 | |a Energy management | ||
| 653 | |a Consumption | ||
| 653 | |a Electricity pricing | ||
| 653 | |a Energy management systems | ||
| 653 | |a Materials durability | ||
| 653 | |a Mixed integer | ||
| 653 | |a Alternative energy sources | ||
| 653 | |a Cost control | ||
| 653 | |a Systems stability | ||
| 700 | 1 | |a Baptista José |u Department of Engineering, University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal; baptista@utad.pt | |
| 700 | 1 | |a Cerveira Adelaide |u INEC-TEC UTAD Pole, University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal | |
| 773 | 0 | |t Algorithms |g vol. 18, no. 10 (2025), p. 656-675 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3265821488/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3265821488/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3265821488/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |