Optimal allocation of hybrid distribution transformers considering dynamical control

Abstract

Hybrid distribution transformer (HDT) is a new kind of smart devices applied in active distribution network (ADN). It has the ability to provide reactive power compensation and ac voltage dynamic regulation. In the context of ADN, the allocation (placement and sizing) and control of HDT are, in fact, coupled issues. Considering this fact, this paper proposes a bi-level programming approach to achieve the optimal allocation of HDTs in a dynamic context. In the upper level, particle swarm optimization (PSO) is used to optimally determine the location and size of HDT installations, with the objective of minimizing HDT investment cost. While in the lower level, model predictive control (MPC) is utilized to design the multi-HDTs’ cooperative control scheme, with the objective of dynamical reactive power optimization. The key feature of this method is that it integrates the allocation and control problem, thus achieving the optimal allocation in a dynamic context. The proposed method is validated using the IEEE 33-bus and 123-bus test system. The results show that it considerably reduces investment cost and improve voltage profile.