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Adaptive Design of Solar-Powered Energy Systems Based on Daily Clearness State Evolution

Author

Listed:
  • Dong Liang

    (State Grid Gansu Electric Power Company Linxia Power Supply Company, Linxia 731100, China)

  • Long Ma

    (State Grid Gansu Electric Power Company Linxia Power Supply Company, Linxia 731100, China)

  • Peng Wang

    (State Grid Gansu Electric Power Company Linxia Power Supply Company, Linxia 731100, China)

  • Yuanxia Li

    (State Grid Gansu Electric Power Company Linxia Power Supply Company, Linxia 731100, China)

  • Yiping Luo

    (State Key Laboratory of Eco-Hydraulic in Northwest Arid Region, Xi’an University of Technology, Xi’an 710048, China)

Abstract

The optimal designing of the hybrid energy system (HES) is a challenging task due to the multiple objectives and various uncertainties. Especially for HES, primarily powered by solar energy, the reference solar radiation data directly impact the result of the optimization design. To incorporate the stochastic characteristics of solar radiation into the sizing process, a data-driven stochastic modeling method for solar radiation is proposed. The method involves two layers of stochastic processes that capture the intraday variation and daily evolution of solar radiation. First, the clearness index (CI) is introduced to describe the radiation intensity at different times. Then, the daily clearness state (DCS) is proposed, based on the statistical indicators of the intraday CI. The Markov model is used to describe the stochastic evolutionary characteristics between different DCSs. The probabilistic distribution of the CI under different DCS is obtained based on the diffusion kernel density estimation (DKDE), which is used for the stochastic generation of the CI at various times of the day. Finally, the radiation profile required for the optimal design is obtained by the stochastic generation of the DCS sequences and the intraday clearness index under corresponding states. A case study of an off-grid solar-powered HES is provided to illustrate this methodology.

Suggested Citation

  • Dong Liang & Long Ma & Peng Wang & Yuanxia Li & Yiping Luo, 2024. "Adaptive Design of Solar-Powered Energy Systems Based on Daily Clearness State Evolution," Energies, MDPI, vol. 17(10), pages 1-17, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:10:p:2328-:d:1392915
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    References listed on IDEAS

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