Multi-Period Fast Robust Optimization of Active and Reactive Power in
Active Distribution Networks
Abstract
Connecting a large number of intermittent distributed generators
(DGs) and electric vehicles (EVs) to a distribution network increases
uncertainty and seriously threatens the safety and economic operation of
that distribution network. Robust optimization is an important method of
hedging against the adverse effects of uncertainty. However, the
existing literature on robust optimization of distribution networks has
not taken into account constraints on the travel distances of regulating
equipment. In response to this deficiency, this paper considers
constraints on the cycle period of energy storage systems (ESSs), travel
distances of switchable capacitors and reactors, and load tap changers
(OLTCs) and step voltage regulators (SVRs). A two-stage multi-period
mixed-integer second-order cone robust model for coordinated active and
reactive power optimization of a distribution network is formulated
based on branch flow equations. There are not any dummy variables in the
second stage model. In contrast to the deficiency of the low
computational rate of the column-and-constraint generation (CCG) method,
an iterative solution to the first and second stage models is proposed
based on the cutting plane. Increases to the variables and constraints
are not needed to solve the first stage model. For the second stage
multi-period model, only the model of each single period needs to be
solved first. Then the results of the two models are accumulated.
Overall, the computational speed is significantly enhanced. The
capabilities of the proposed method are validated by three simulation
cases.