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A prominent challenge to the safe and optimal operation of the modern powergrid arises due to growing uncertainties in loads and renewables. Stochasticoptimal power flow (SOPF) formulations provide a mechanism to handle theseuncertainties by computing dispatch decisions and control policies thatmaintain feasibility under uncertainty. Most SOPF formulations consider simplecontrol policies such as affine policies that are mathematically simple andresemble many policies used in current practice. Motivated by the efficacy ofmachine learning (ML) algorithms and the potential benefits of general controlpolicies for cost and constraint enforcement, we put forth a deep neuralnetwork (DNN)-based policy that predicts the generator dispatch decisions inreal time in response to uncertainty. The weights of the DNN are learnt usingstochastic primal-dual updates that solve the SOPF without the need for priorgeneration of training labels and can explicitly account for the feasibilityconstraints in the SOPF. The advantages of the DNN policy over simpler policiesand their efficacy in enforcing safety limits and producing near optimalsolutions are demonstrated in the context of a chance constrained formulationon a number of test cases.

DNN-based Policies for Stochastic AC OPF