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A comparison of metaheuristics for the optimal capacity planning of an isolated, battery-less, hydrogen-based micro-grid.

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Title: A comparison of metaheuristics for the optimal capacity planning of an isolated, battery-less, hydrogen-based micro-grid.
Authors: Mohseni, Soheil1 (AUTHOR) soheil.mohseni@ecs.vuw.ac.nz, Brent, Alan C.1 (AUTHOR), Burmester, Daniel1 (AUTHOR)
Source: Applied Energy. Feb2020, Vol. 259, pN.PAG-N.PAG. 1p.
Subjects: Renewable energy sources, Particle swarm optimization, Capacity requirements planning, Metaheuristic algorithms, Sustainable development, Energy development, Genetic algorithms
Geographic Terms: New Zealand
Abstract: • A metaheuristic-based model is proposed to optimally size a hydrogen-based microgrid. • The microgrid system is equipped with an innovative hydrogen refuelling station. • The performances of eight metaheuristics are studied in terms of accuracy and speed. • The moth-flame optimizer is found to outperform the popular algorithms in this area. • The system's levelized costs of electricity and hydrogen are $0.09/kWh and $4.61/kg. There has been growing interest in the development of sustainable energy systems using the potential of renewable energy sources. However, due to the intermittent nature of renewable energy sources, they must be accompanied by appropriate storage devices to be optimally integrated into so-called smart micro-grid systems. The optimal design problem of sustainable micro-grids is associated with several nonlinearities and non-convexities, and therefore is not amenable to exact methods of optimization. Accordingly, this paper proposes a metaheuristic-based approach for optimizing the size and typology of the components of an off-grid hydrogen-based micro-grid, backed up with super-capacitors and stationary fuel cell systems, in the presence of a hydrogen refuelling station. The paper also compares the performance of six recent metaheuristics. The simulations are carried out for the climatic conditions of the Feilding area, New Zealand using MATLAB. Based on the comparative results, the moth-flame optimization algorithm is found to result in a significant reduction in the total net present cost of the system in comparison with other investigated algorithms. Notably, it outperforms the genetic algorithm and the particle swarm optimization in terms of solution quality by ~2.1% and ~3.2% (equating to cost savings of $123,910 and $188,129 from the target system). The results also demonstrate both the technical feasibility and cost-effectiveness of the proposed stand-alone micro-grid architecture. Particularly, the levelized costs of electricity and hydrogen of the conceptualized system are found to be $0.09/kWh and $4.61/kg, respectively, which are well below the current tariffs in New Zealand. [ABSTRACT FROM AUTHOR]
Copyright of Applied Energy is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 141117469
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: A comparison of metaheuristics for the optimal capacity planning of an isolated, battery-less, hydrogen-based micro-grid.
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  Data: • A metaheuristic-based model is proposed to optimally size a hydrogen-based microgrid. • The microgrid system is equipped with an innovative hydrogen refuelling station. • The performances of eight metaheuristics are studied in terms of accuracy and speed. • The moth-flame optimizer is found to outperform the popular algorithms in this area. • The system's levelized costs of electricity and hydrogen are $0.09/kWh and $4.61/kg. There has been growing interest in the development of sustainable energy systems using the potential of renewable energy sources. However, due to the intermittent nature of renewable energy sources, they must be accompanied by appropriate storage devices to be optimally integrated into so-called smart micro-grid systems. The optimal design problem of sustainable micro-grids is associated with several nonlinearities and non-convexities, and therefore is not amenable to exact methods of optimization. Accordingly, this paper proposes a metaheuristic-based approach for optimizing the size and typology of the components of an off-grid hydrogen-based micro-grid, backed up with super-capacitors and stationary fuel cell systems, in the presence of a hydrogen refuelling station. The paper also compares the performance of six recent metaheuristics. The simulations are carried out for the climatic conditions of the Feilding area, New Zealand using MATLAB. Based on the comparative results, the moth-flame optimization algorithm is found to result in a significant reduction in the total net present cost of the system in comparison with other investigated algorithms. Notably, it outperforms the genetic algorithm and the particle swarm optimization in terms of solution quality by ~2.1% and ~3.2% (equating to cost savings of $123,910 and $188,129 from the target system). The results also demonstrate both the technical feasibility and cost-effectiveness of the proposed stand-alone micro-grid architecture. Particularly, the levelized costs of electricity and hydrogen of the conceptualized system are found to be $0.09/kWh and $4.61/kg, respectively, which are well below the current tariffs in New Zealand. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Applied Energy is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.apenergy.2019.114224
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        Text: English
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        StartPage: N.PAG
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      – SubjectFull: Renewable energy sources
        Type: general
      – SubjectFull: Particle swarm optimization
        Type: general
      – SubjectFull: Capacity requirements planning
        Type: general
      – SubjectFull: Metaheuristic algorithms
        Type: general
      – SubjectFull: Sustainable development
        Type: general
      – SubjectFull: Energy development
        Type: general
      – SubjectFull: Genetic algorithms
        Type: general
      – SubjectFull: New Zealand
        Type: general
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      – TitleFull: A comparison of metaheuristics for the optimal capacity planning of an isolated, battery-less, hydrogen-based micro-grid.
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            NameFull: Mohseni, Soheil
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            NameFull: Brent, Alan C.
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            NameFull: Burmester, Daniel
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              Text: Feb2020
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