Data-Adaptive Robust Optimization Method for the Economic Dispatch of Active Distribution Networks
Date of Original Version
Due to the restricted mathematical description of the uncertainty set, the current two-stage robust optimization is usually over-conservative which has drawn concerns from power system operators. This paper proposes a novel data-adaptive robust optimization method for the economic dispatch of active distribution network with renewables. The scenario-generation method and two-stage robust optimization are combined into the proposed method. To reduce the conservativeness, a few extreme scenarios selected from historical data are used to replace the conventional uncertainty set. The proposed extreme-scenario selection algorithm takes advantage of considering the correlations and can be adaptive to different historical data sets. A theoretical proof is given that the constraints will be satisfied under all possible scenarios if they hold in the selected extreme scenarios, which guarantees the robustness of the decision. Numerical results demonstrate that the proposed data-adaptive robust optimization algorithm with the selected uncertainty set is less conservative but equally as robust as the existing two-stage robust optimization approaches. This leads to the improved economy of the decision with uncompromised security.
IEEE Transactions on Smart Grid
Zhang, Yipu, Xiaomeng Ai, Jinyu Wen, Jiakun Fang, and Haibo He. "Data-Adaptive Robust Optimization Method for the Economic Dispatch of Active Distribution Networks." IEEE Transactions on Smart Grid 10, 4 (2019): 3791-3800. doi:10.1109/TSG.2018.2834952.