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Data-driven industrial park microgrids robust optimization method
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DOI：10.3389/fenrg.2025.1535211.png)
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Abstract
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In order to accurately describe the impact of the volatility and randomness of renewable energy output power on the operation of industrial park microgrids; a data-driven robust optimization method for industrial park microgrids is proposed. Firstly; based on the traditional interval set; the uncertain parameters of renewable energy output are modeled using a polyhedral set. Then; an ellipsoidal uncertainty set is established using historical data of renewable energy output. By connecting high-dimensional ellipsoidal vertices; a data-driven convex hull polyhedron set is established. Then; the uncertain parameters are better enveloped by scaling the convex hull set. A data-driven robust optimization model for industrial park microgrid was further established; and the column and constraint (C&CG) generation algorithm was used to solve the model. Finally; simulation comparisons were conducted through examples; and the results showed that the data-driven industrial park microgrids robust optimization method can reduce conservatism and improve the robustness of optimization results; demonstrating the effectiveness of the proposed method.
Keywords:
renewable energy
microgrid
robust optimization
uncertainty modeling
data-driven method
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