Data-adaptive robust coordinated optimization of dynamic active and reactive power flow in active distribution networks

Abstract

The uncertainty of wind power brings security and economic issues in active distribution networks. As an effective solution to these problems, the active/reactive optimization aims at minimizing the total operation cost and network loss by controlling multiple resources. This paper proposes a two-stage data-adaptive robust optimization model for the dynamic active-reactive power flow. The dynamic correlation between the uncertainties of active and reactive power is analyzed at two kinds of power factor modes of wind turbines and then is described by a linear affine decision rule. To characterize these uncertainties, a data-adaptive multi-band uncertainty set is constructed based on historical data, which allows the number of bands and the corresponding weight coefficients to be adjusted under different confidence levels. Subsequently, the conic relaxation and linearization techniques are performed to reformulate the robust optimization model as a mixed-integer second-order cone programming problem. A two-level solution framework, consisting of a column-and-constraint generation algorithm and an outer approximation algorithm, is employed to solve this problem. Simulation results on a modified IEEE 33-bus system indicate that the variable power factor mode is more suitable for describing the dynamic correlation than the constant power factor mode. Besides, the proposed model and method also show better practicality and solution efficiency than the existing ones.