A Distributionally Robust Reactive Power Optimization Model for Active Distribution Network Considering Reactive Power Support of DG and Switch Reconfiguration

In recent years, the rapid development of the Distributed Generation (DG) has brought new challenges to the reactive power optimization in the active distribution network. The traditional stochastic optimization and robust optimization methods may result in over-conservative or over-risky decisions when dealing with the uncertainty of renewable energies. Therefore, this paper proposes a distributionally robust reactive power optimization model for active distribution network, which regards the network loss as the its optimization target, and considers the reactive power support of and the switch reconfiguration. This model includes not only switching, on load tap changer (OLTC), discrete and continuous reactive power compensation devices usually contained in traditional reactive power optimization, but also the detailed model of capacity curve of Double-Fed Induction Generator and Micro-Gas Turbine. After the reactive power support is integrated, the linearization method of active and reactive power output coupling characteristics constraint is specially designed. Then, based on the typical scenario data of wind and photovoltaic power, and the regulation characteristics for different decision variables, a data-driven based two-stage distributionally robust reactive power optimization model is set up, in which the uncertainty probability distribution confidence set is simultaneously constrained by 1-norm and co-norm. Finally, by the Columns and Constraints Generation algorithm, the IEEE 33-bus system is applied to verify the effectiveness of proposed. (C) 2019 The Authors. Published by Elsevier Ltd.