Optimizing Renewable Microgrid Performance Through Hydrogen Storage Integration

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Algorithms vol. 18, no. 10 (2025), p. 656-675
1. Verfasser: Ribeiro, Bruno
Weitere Verfasser: Baptista José, Cerveira Adelaide
Veröffentlicht:
MDPI AG
Schlagworte:
Online-Zugang:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Tags: Tag hinzufügen
Keine Tags, Fügen Sie das erste Tag hinzu!

MARC

LEADER 00000nab a2200000uu 4500
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