Distributionally Robust Optimal Reactive Power Dispatch with Wasserstein Distance in Active Distribution Network

• Main Authors: Jun Liu, Yefu Chen, Chao Duan, Jiang Lin, Jia Lyu
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: Journal of Modern Power Systems and Clean Energy
• Subjects: Active distribution network; chance constraint; renewable energy source; optimal reactive power dispatch (ORPD)
• Online Access: https://ieeexplore.ieee.org/document/9097573/

The uncertainties from renewable energy sources (RESs) will not only introduce significant influences to active power dispatch, but also bring great challenges to the analysis of optimal reactive power dispatch (ORPD). To address the influence of high penetration of RES integrated into active distribution networks, a distributionally robust chance constraint (DRCC)-based ORPD model considering discrete reactive power compensators is proposed in this paper. The proposed ORPD model combines a second-order cone programming (SOCP)-based model at the nominal operation mode and a linear power flow (LPF) model to reflect the system response under certainties. Then, a distributionally robust optimization (WDRO) method with Wasserstein distance is utilized to solve the proposed DRCC-based ORPD model. The WDRO method is data-driven due to the reason that the ambiguity set is constructed by the available historical data without any assumption on the specific probability distribution of the uncertainties. And the more data is available, the smaller the ambiguity would be. Numerical results on IEEE 30-bus and 123-bus systems and comparisons with the other three-benchmark approaches demonstrate the accuracy and effectiveness of the proposed model and method.