  Prediction plays a vital role in the active distribution network voltage
regulation under the high penetration of photovoltaics. Current prediction
models aim at minimizing individual prediction errors but overlook their
collective impacts on downstream decision-making. Hence, this paper proposes a
safety-aware semi-end-to-end coordinated decision model to bridge the gap from
the downstream voltage regulation to the upstream multiple prediction models in
a coordinated differential way. The semi-end-to-end model maps the input
features to the optimal var decisions via prediction, decision-making, and
decision-evaluating layers. It leverages the neural network and the
second-order cone program (SOCP) to formulate the stochastic PV/load
predictions and the var decision-making/evaluating separately. Then the var
decision quality is evaluated via the weighted sum of the power loss for
economy and the voltage violation penalty for safety, denoted by regulation
loss. Based on the regulation loss and prediction errors, this paper proposes
the hybrid loss and hybrid stochastic gradient descent algorithm to
back-propagate the gradients of the hybrid loss with respect to multiple
predictions for enhancing decision quality. Case studies verify the
effectiveness of the proposed model with lower power loss for economy and lower
voltage violation rate for safety awareness.
