Abstract:

The integration of a high proportion of distributed renewable energy sources (DRES) and a substantial number of fully controllable flexible power electronic devices has brought more clean electrical energy and control options to traditional distribution networks. However, the uneven temporal and spatial distribution of their output power and the complexity of regulating a vast number of devices pose significant challenges to the operation of distribution networks. In response to this, a distributionally robust optimization strategy for flexible distribution networks, considering the multiple correlations of renewable energy, is proposed. Firstly, aiming to minimize active power loss and voltage deviation in the distribution network, an optimal power flow model for flexible distribution networks is derived and constructed, incorporating various coordinated control measures for sources, grids and loads. Secondly, taking into account the multiple correlations of renewable energy in terms of time, space and power dimensions, a two-stage distributionally robust optimization model for flexible distribution networks is established based on data-driven approaches. The 1-norm and ∞-norm are employed to describe the uncertainty sets of sources and loads, and second-order cones are utilized for linearization and convex relaxation. Finally, the column and constraint generation algorithm is adopted to solve the model, and a simulation analysis is conducted using an improved IEEE 33-bus test system as an example, verifying the effectiveness and practicality of the proposed method.

Key words: flexible distribution network, renewable energy, distributionally robust optimization, data-driven, second-order cone programming, column-and-constraint generation algorithm