AE 2020 Mohseni et al.pdf (4.91 MB)

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

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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.


Preferred citation

Mohseni, S., Brent, A. C. & Burmester, D. (2020). A comparison of metaheuristics for the optimal capacity planning of an isolated, battery-less, hydrogen-based micro-grid. Applied Energy, 259, 114224-114224.

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Applied Energy



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Elsevier BV

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