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Optimal Siting and Sizing of Hydrogen Production Modules in Distribution Networks with Photovoltaic Uncertainties

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  • Zhiyong Li

    (Department of Computer Technology and Application, Qinghai University, Xining 810016, China)

  • Wenbin Wu

    (Qinghai Key Lab of Efficient Utilization of Clean Energy, School of Energy and Electrical Engineering, University of Qinghai, Xining 810016, China)

  • Yang Si

    (Qinghai Key Lab of Efficient Utilization of Clean Energy, School of Energy and Electrical Engineering, University of Qinghai, Xining 810016, China)

  • Xiaotao Chen

    (Qinghai Key Lab of Efficient Utilization of Clean Energy, School of Energy and Electrical Engineering, University of Qinghai, Xining 810016, China)

Abstract

Hydrogen production modules (HPMs) play a crucial role in harnessing abundant photovoltaic power by producing and supplying hydrogen to factories, resulting in significant operational cost reductions and efficient utilization of the photovoltaic panel output. However, the output of photovoltaic power is stochastic, which will affect the revenue of investing in an HPM. This paper presents a comprehensive analysis of HPMs, starting with the modeling of their operational process and investigating their influence on distribution system operations. Building upon these discussions, a deterministic optimization model is established to address the corresponding challenges. Furthermore, a two-stage stochastic planning model is proposed to determine optimal locations and sizes of HPMs in distribution systems, accounting for uncertainties. The objective of the two-stage stochastic planning model is to minimize the distribution system’s operational costs plus the investment costs of the HPM subject to power flow constraints. To tackle the stochastic nature of photovoltaic power, a data-driven algorithm is introduced to cluster historical data into representative scenarios, effectively reducing the planning model’s scale. To ensure an efficient solution, a Benders’ decomposition-based algorithm is proposed, which is an iterative method with a fast convergence speed. The proposed model and algorithms are validated using a widely utilized IEEE 33-bus system through numerical experiments, demonstrating the optimality of the HPM plan generated by the algorithm. The proposed model and algorithms offer an effective approach for decision-makers in managing uncertainties and optimizing HPM deployment, paving the way for sustainable and efficient energy solutions in distribution systems. Sensitivity analysis verifies the optimality of the HPM’s siting and sizing obtained by the proposed algorithm, which also reveals immense economic and environmental benefits.

Suggested Citation

  • Zhiyong Li & Wenbin Wu & Yang Si & Xiaotao Chen, 2023. "Optimal Siting and Sizing of Hydrogen Production Modules in Distribution Networks with Photovoltaic Uncertainties," Energies, MDPI, vol. 16(22), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:22:p:7636-:d:1282648
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    References listed on IDEAS

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    1. Jongbeom Kwak & Haktae Lee & Somin Park & Jaehyuk Park & Seungho Jung, 2023. "Risk Assessment of a Hydrogen Refueling Station in an Urban Area," Energies, MDPI, vol. 16(9), pages 1-18, May.
    2. Eriksson, E.L.V. & Gray, E.MacA., 2017. "Optimization and integration of hybrid renewable energy hydrogen fuel cell energy systems – A critical review," Applied Energy, Elsevier, vol. 202(C), pages 348-364.
    3. Mouli-Castillo, Julien & Heinemann, Niklas & Edlmann, Katriona, 2021. "Mapping geological hydrogen storage capacity and regional heating demands: An applied UK case study," Applied Energy, Elsevier, vol. 283(C).
    4. Bassamzadeh, Nastaran & Ghanem, Roger, 2017. "Multiscale stochastic prediction of electricity demand in smart grids using Bayesian networks," Applied Energy, Elsevier, vol. 193(C), pages 369-380.
    5. Tania Itzel Serrano-Arévalo & Javier Tovar-Facio & José María Ponce-Ortega, 2023. "Optimal Incorporation of Intermittent Renewable Energy Storage Units and Green Hydrogen Production in the Electrical Sector," Energies, MDPI, vol. 16(6), pages 1-25, March.
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