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A stochastic optimization approach to the design and operation planning of a hybrid renewable energy system

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  • Yu, Jiah
  • Ryu, Jun-Hyung
  • Lee, In-beum

Abstract

Hybrid renewable energy systems (HRESs) have been introduced globally with the increasing emphasis on sustainable energy and the environment. It is very challenging to manage HRESs due to the inherent uncertainty in energy supply and demand. Recently, Energy Storage Systems (ESSs) have been drawing increasing attention as a promising alternative to minimize the difference between varying supply and demand. The ESS should be designed and operated based on the explicit consideration of uncertainty because a deterministic approach only captures a fixed snapshot of the varying system. The resulting scheduling problem for ESS operation was formulated as a two-stage stochastic programming model in this study. The model was then transformed into a mixed integer linear programming problem based on multiple equivalent scenarios. Five different scenario-generation methodologies were employed to illustrate the applicability of the approach. A numerical example illustrates that the HRES design and operation cost according to a stochastic model (US$ 6981/day) was at least 9.1% more economical than deterministic model (US$ 7680/day). From the results, it is shown that the proposed approach results in intelligent ESS operation that can increase the applicability of the HRES.

Suggested Citation

  • Yu, Jiah & Ryu, Jun-Hyung & Lee, In-beum, 2019. "A stochastic optimization approach to the design and operation planning of a hybrid renewable energy system," Applied Energy, Elsevier, vol. 247(C), pages 212-220.
    • Handle: RePEc:eee:appene:v:247:y:2019:i:c:p:212-220
    DOI: 10.1016/j.apenergy.2019.03.207
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    References listed on IDEAS

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    1. Zhou, Zhe & Zhang, Jianyun & Liu, Pei & Li, Zheng & Georgiadis, Michael C. & Pistikopoulos, Efstratios N., 2013. "A two-stage stochastic programming model for the optimal design of distributed energy systems," Applied Energy, Elsevier, vol. 103(C), pages 135-144.
    2. Narayan, Apurva & Ponnambalam, Kumaraswamy, 2017. "Risk-averse stochastic programming approach for microgrid planning under uncertainty," Renewable Energy, Elsevier, vol. 101(C), pages 399-408.
    3. Shin, Joohyun & Lee, Jay H. & Realff, Matthew J., 2017. "Operational planning and optimal sizing of microgrid considering multi-scale wind uncertainty," Applied Energy, Elsevier, vol. 195(C), pages 616-633.
    4. Bhattacharjee, Vikram & Khan, Irfan, 2018. "A non-linear convex cost model for economic dispatch in microgrids," Applied Energy, Elsevier, vol. 222(C), pages 637-648.
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