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A hybrid multi-level optimization approach for the dynamic synthesis/design and operation/control under uncertainty of a fuel cell system

Author

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  • Kim, Kihyung
  • von Spakovsky, Michael R.
  • Wang, M.
  • Nelson, Douglas J.

Abstract

During system development, large-scale, complex energy systems require multi-disciplinary efforts to achieve system quality, cost, and performance goals. As systems become larger and more complex, the number of possible system configurations and technologies, which meet the designer’s objectives optimally, increases greatly. In addition, both transient and environmental effects may need to be taken into account. Thus, the difficulty of developing the system via the formulation of a single optimization problem in which the optimal synthesis/design and operation/control of the system are achieved simultaneously is great and rather problematic. This difficulty is further heightened with the introduction of uncertainty analysis, which transforms the problem from a purely deterministic one into a probabilistic one. Uncertainties, system complexity and nonlinearity, and large numbers of decision variables quickly render the single optimization problem unsolvable by conventional, single-level, optimization strategies.

Suggested Citation

  • Kim, Kihyung & von Spakovsky, Michael R. & Wang, M. & Nelson, Douglas J., 2011. "A hybrid multi-level optimization approach for the dynamic synthesis/design and operation/control under uncertainty of a fuel cell system," Energy, Elsevier, vol. 36(6), pages 3933-3943.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:6:p:3933-3943
    DOI: 10.1016/j.energy.2010.08.024
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    References listed on IDEAS

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    1. Toffolo, A. & Lazzaretto, A., 2002. "Evolutionary algorithms for multi-objective energetic and economic optimization in thermal system design," Energy, Elsevier, vol. 27(6), pages 549-567.
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    Cited by:

    1. Cuneo, A. & Zaccaria, V. & Tucker, D. & Traverso, A., 2017. "Probabilistic analysis of a fuel cell degradation model for solid oxide fuel cell and gas turbine hybrid systems," Energy, Elsevier, vol. 141(C), pages 2277-2287.
    2. Qian, Fanyue & Gao, Weijun & Yang, Yongwen & Yu, Dan, 2020. "Potential analysis of the transfer learning model in short and medium-term forecasting of building HVAC energy consumption," Energy, Elsevier, vol. 193(C).
    3. George N. Sakalis & George J. Tzortzis & Christos A. Frangopoulos, 2019. "Intertemporal Static and Dynamic Optimization of Synthesis, Design, and Operation of Integrated Energy Systems of Ships," Energies, MDPI, vol. 12(5), pages 1-50, March.
    4. Chen, Xi & Yang, Hongxing & Sun, Ke, 2016. "A holistic passive design approach to optimize indoor environmental quality of a typical residential building in Hong Kong," Energy, Elsevier, vol. 113(C), pages 267-281.
    5. Chen, Xi & Yang, Hongxing & Wang, Yuanhao, 2017. "Parametric study of passive design strategies for high-rise residential buildings in hot and humid climates: miscellaneous impact factors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 442-460.
    6. Alexandros Arsalis & George E. Georghiou, 2018. "A Decentralized, Hybrid Photovoltaic-Solid Oxide Fuel Cell System for Application to a Commercial Building," Energies, MDPI, vol. 11(12), pages 1-20, December.
    7. Frangopoulos, Christos A., 2018. "Recent developments and trends in optimization of energy systems," Energy, Elsevier, vol. 164(C), pages 1011-1020.
    8. Chen, Xi & Yang, Hongxing & Sun, Ke, 2017. "Developing a meta-model for sensitivity analyses and prediction of building performance for passively designed high-rise residential buildings," Applied Energy, Elsevier, vol. 194(C), pages 422-439.

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