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Distributed Robust Optimal Dispatch for the Microgrid Considering Output Correlation between Wind and Photovoltaic

Ming Li1,*, Cairen Furifu1, Chengyang Ge2, Yunping Zheng1, Shunfu Lin2, Ronghui Liu2

1 New Power System Technology Research Institute, Electric Power Research Institute, The Electric Power Research Institute of State Grid Xinjiang Electric Power Co., Ltd., Urumqi, 830000, China
2 The College of Electrical Engineering, Shanghai University of Electric Power, Shanghai, 200090, China

* Corresponding Author: Ming Li. Email: email

(This article belongs to this Special Issue: Key Technologies of Renewable Energy Consumption and Optimal Operation under )

Energy Engineering 2023, 120(8), 1775-1801.


As an effective carrier of integrated clean energy, the microgrid has attracted wide attention. The randomness of renewable energies such as wind and solar power output brings a significant cost and impact on the economics and reliability of microgrids. This paper proposes an optimization scheme based on the distributionally robust optimization (DRO) model for a microgrid considering solar-wind correlation. Firstly, scenarios of wind and solar power output scenarios are generated based on non-parametric kernel density estimation and the Frank-Copula function; then the generated scenario results are reduced by K-means clustering; finally, the probability confidence interval of scenario distribution is constrained by 1-norm and ∞-norm. The model is solved by a column-and-constraint generation algorithm. Experimental studies are conducted on a microgrid system in Jiangsu, China and the obtained scheduling solution turned out to be superior under wind and solar power uncertainties, which verifies the effectiveness of the proposed DRO model.


Cite This Article

Li, M., Furifu, C., Ge, C., Zheng, Y., Lin, S. et al. (2023). Distributed Robust Optimal Dispatch for the Microgrid Considering Output Correlation between Wind and Photovoltaic. Energy Engineering, 120(8), 1775–1801.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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