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Robust design and optimization for autonomous PV-wind hybrid power systems

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Abstract

This study presents a robust design method for autonomous photovoltaic (PV)-wind hybrid power systems to obtain an optimum system configuration insensitive to design variable variations. This issue has been formulated as a constraint multi-objective optimization problem, which is solved by a multi-objective genetic algorithm, NSGA-II. Monte Carlo Simulation (MCS) method, combined with Latin Hypercube Sampling (LHS), is applied to evaluate the stochastic system performance. The potential of the proposed method has been demonstrated by a conceptual system design. A comparative study between the proposed robust method and the deterministic method presented in literature has been conducted. The results indicate that the proposed method can find a large mount of Pareto optimal system configurations with better compromising performance than the deterministic method. The trade-off information may be derived by a systematical comparison of these configurations. The proposed robust design method should be useful for hybrid power systems that require both optimality and robustness.

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Authors and Affiliations

  1. Institute of Fuel Cells, Shanghai Jiao Tong University, Shanghai, 200240, China

    Jun-hai Shi, Zhi-dan Zhong, Xin-jian Zhu & Guang-yi Cao

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  1. Jun-hai Shi
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  2. Zhi-dan Zhong
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  3. Xin-jian Zhu
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  4. Guang-yi Cao
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Correspondence to Jun-hai Shi.

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Shi, Jh., Zhong, Zd., Zhu, Xj. et al. Robust design and optimization for autonomous PV-wind hybrid power systems. J. Zhejiang Univ. Sci. A 9, 401–409 (2008). https://doi.org/10.1631/jzus.A071317

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  • DOI: https://doi.org/10.1631/jzus.A071317

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