Robust design and optimization for autonomous PV-wind hybrid power systems |
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| Authors: | Jun-hai Shi Zhi-dan Zhong Xin-jian Zhu Guang-yi Cao |
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| Institution: | (1) Institute of Fuel Cells, Shanghai Jiao Tong University, Shanghai, 200240, China |
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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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| Keywords: | PV-wind power system Robust design Constraint multi-objective optimizations Multi-objective genetic algorithms Monte Carlo Simulation (MCS) Latin Hypercube Sampling (LHS) |
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