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Prediction plays a vital role in the active distribution network voltageregulation under the high penetration of photovoltaics. Current predictionmodels aim at minimizing individual prediction errors but overlook theircollective impacts on downstream decision-making. Hence, this paper proposes asafety-aware semi-end-to-end coordinated decision model to bridge the gap fromthe downstream voltage regulation to the upstream multiple prediction models ina coordinated differential way. The semi-end-to-end model maps the inputfeatures to the optimal var decisions via prediction, decision-making, anddecision-evaluating layers. It leverages the neural network and thesecond-order cone program (SOCP) to formulate the stochastic PV/loadpredictions and the var decision-making/evaluating separately. Then the vardecision quality is evaluated via the weighted sum of the power loss foreconomy and the voltage violation penalty for safety, denoted by regulationloss. Based on the regulation loss and prediction errors, this paper proposesthe hybrid loss and hybrid stochastic gradient descent algorithm toback-propagate the gradients of the hybrid loss with respect to multiplepredictions for enhancing decision quality. Case studies verify theeffectiveness of the proposed model with lower power loss for economy and lowervoltage violation rate for safety awareness.

Safety-aware Semi-end-to-end Coordinated Decision Model for Voltage Regulation in Active Distribution Network