  This paper proposes a data-driven affinely adjustable robust Volt/VAr control
(AARVVC) scheme, which modulates the smart inverter reactive power in an affine
function of its active power, based on the voltage sensitivities with respect
to real/reactive power injections. To achieve a fast and accurate estimation of
voltage sensitivities, we propose a data-driven method based on deep neural
network (DNN), together with a rule-based bus-selection process using the
bidirectional search method. Our method only uses the operating statuses of
selected buses as inputs to DNN, thus significantly improving the training
efficiency and reducing information redundancy. Finally, a distributed
consensus-based solution, based on the alternating direction method of
multipliers (ADMM), for the AARVVC is applied to decide the inverter reactive
power adjustment rule with respect to its active power. Only limited
information exchange is required between each local agent and the central agent
to obtain the slope of the reactive power adjustment rule, and there is no need
for the central agent to solve any (sub)optimization problems. Numerical
results on the modified IEEE-123 bus system validate the effectiveness and
superiority of the proposed data-driven AARVVC method.
