Review of energy management systems and optimization methods for hydrogen‐based hybrid building microgrids
Abstract Renewable energy‐based microgrids (MGs) strongly depend on the implementation of energy storage technologies to optimize their functionality. Traditionally, electrochemical batteries have been the predominant means of energy storage. However, technological advancements have led to the recog...
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| Format: | Article |
| Language: | English |
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Wiley
2024-08-01
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| Series: | Energy Conversion and Economics |
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| Online Access: | https://doi.org/10.1049/enc2.12126 |
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| _version_ | 1846164456549122048 |
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| author | Fahad Ali Sarwar Ignacio Hernando‐Gil Ionel Vechiu |
| author_facet | Fahad Ali Sarwar Ignacio Hernando‐Gil Ionel Vechiu |
| author_sort | Fahad Ali Sarwar |
| collection | DOAJ |
| description | Abstract Renewable energy‐based microgrids (MGs) strongly depend on the implementation of energy storage technologies to optimize their functionality. Traditionally, electrochemical batteries have been the predominant means of energy storage. However, technological advancements have led to the recognition of hydrogen as a promising solution to address the long‐term energy requirements of microgrid systems. This study conducted a comprehensive literature review aimed at analysing and synthesizing the principal optimization and control methodologies employed in hydrogen‐based microgrids within the context of building microgrid infrastructures. A comparative assessment was conducted to evaluate the merits and disadvantages of the different approaches. The optimization techniques for energy management are categorized based on their predictability, deployment feasibility, and computational complexity. In addition, the proposed ranking system facilitates an understanding of its suitability for diverse applications. This review encompasses deterministic, stochastic, and cutting‐edge methodologies, such as machine learning‐based approaches, and compares and discusses their respective merits. The key outcome of this research is the classification of various energy management strategy methodologies for hydrogen‐based MG, along with a mechanism to identify which methodologies will be suitable under what conditions. Finally, a detailed examination of the advantages and disadvantages of various strategies for controlling and optimizing hybrid microgrid systems with an emphasis on hydrogen utilization is provided. |
| format | Article |
| id | doaj-art-29b1ece4d89b45908e45a3387a4cd7ea |
| institution | Kabale University |
| issn | 2634-1581 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Energy Conversion and Economics |
| spelling | doaj-art-29b1ece4d89b45908e45a3387a4cd7ea2024-11-18T04:46:08ZengWileyEnergy Conversion and Economics2634-15812024-08-015425927910.1049/enc2.12126Review of energy management systems and optimization methods for hydrogen‐based hybrid building microgridsFahad Ali Sarwar0Ignacio Hernando‐Gil1Ionel Vechiu2ESTIA‐Institute of Technology, EstiaR University of Bordeaux Bidart FranceESTIA‐Institute of Technology, EstiaR University of Bordeaux Bidart FranceESTIA‐Institute of Technology, EstiaR University of Bordeaux Bidart FranceAbstract Renewable energy‐based microgrids (MGs) strongly depend on the implementation of energy storage technologies to optimize their functionality. Traditionally, electrochemical batteries have been the predominant means of energy storage. However, technological advancements have led to the recognition of hydrogen as a promising solution to address the long‐term energy requirements of microgrid systems. This study conducted a comprehensive literature review aimed at analysing and synthesizing the principal optimization and control methodologies employed in hydrogen‐based microgrids within the context of building microgrid infrastructures. A comparative assessment was conducted to evaluate the merits and disadvantages of the different approaches. The optimization techniques for energy management are categorized based on their predictability, deployment feasibility, and computational complexity. In addition, the proposed ranking system facilitates an understanding of its suitability for diverse applications. This review encompasses deterministic, stochastic, and cutting‐edge methodologies, such as machine learning‐based approaches, and compares and discusses their respective merits. The key outcome of this research is the classification of various energy management strategy methodologies for hydrogen‐based MG, along with a mechanism to identify which methodologies will be suitable under what conditions. Finally, a detailed examination of the advantages and disadvantages of various strategies for controlling and optimizing hybrid microgrid systems with an emphasis on hydrogen utilization is provided.https://doi.org/10.1049/enc2.12126building microgridsenergy management systemsenergy storagehydrogen storageoptimization methodsreinforcement learning |
| spellingShingle | Fahad Ali Sarwar Ignacio Hernando‐Gil Ionel Vechiu Review of energy management systems and optimization methods for hydrogen‐based hybrid building microgrids Energy Conversion and Economics building microgrids energy management systems energy storage hydrogen storage optimization methods reinforcement learning |
| title | Review of energy management systems and optimization methods for hydrogen‐based hybrid building microgrids |
| title_full | Review of energy management systems and optimization methods for hydrogen‐based hybrid building microgrids |
| title_fullStr | Review of energy management systems and optimization methods for hydrogen‐based hybrid building microgrids |
| title_full_unstemmed | Review of energy management systems and optimization methods for hydrogen‐based hybrid building microgrids |
| title_short | Review of energy management systems and optimization methods for hydrogen‐based hybrid building microgrids |
| title_sort | review of energy management systems and optimization methods for hydrogen based hybrid building microgrids |
| topic | building microgrids energy management systems energy storage hydrogen storage optimization methods reinforcement learning |
| url | https://doi.org/10.1049/enc2.12126 |
| work_keys_str_mv | AT fahadalisarwar reviewofenergymanagementsystemsandoptimizationmethodsforhydrogenbasedhybridbuildingmicrogrids AT ignaciohernandogil reviewofenergymanagementsystemsandoptimizationmethodsforhydrogenbasedhybridbuildingmicrogrids AT ionelvechiu reviewofenergymanagementsystemsandoptimizationmethodsforhydrogenbasedhybridbuildingmicrogrids |